mahler measures,short walksand log-sine integrals · 48. log-sine integrals 9. multiple...

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4. Introduction 17. Short Random Walks 41. Multiple Mahler Measures 48. Log-sine Integrals Mahler Measures, Short Walks and Log-sine Integrals A case study in hybrid computation Jonathan M. Borwein frsc faa faaas Laureate Professor & Director of CARMA, Univ. of Newcastle this talk: http://carma.newcastle.edu.au/jon/handbook.pdf SNC 2011 San Jose, June 7-9, 2011 Revised: June 7, 2011 armin straub: issac student prize: http://carma.newcastle.edu.au/jon/logsin3.pdf J.M. Borwein Mahler Measures

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Page 1: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

Mahler Measures, Short Walks andLog-sine Integrals

A case study in hybrid computation

Jonathan M. Borwein frsc faa faaas

Laureate Professor & Director of CARMA, Univ. of Newcastlethis talk: http://carma.newcastle.edu.au/jon/handbook.pdf

SNC 2011San Jose, June 7-9, 2011

Revised: June 7, 2011armin straub: issac student prize: http://carma.newcastle.edu.au/jon/logsin3.pdf

J.M. Borwein Mahler Measures

Page 2: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

Contents. We will cover some of the following:

1 4. Introduction8. Multiple Polylogarithms9. Log-sine Integrals10. Random Walks15. Mahler Measures16. Carlson’s Theorem

2 17. Short Random Walks18. Combinatorics24. Meijer-G functions29. Hypergeometric values of W3,W432. Probability and Bessel J40. Derivative values of W3,W4

3 41. Multiple Mahler Measures42. Relations to η43. Smyth’s results revisited45. Boyd’s Conjectures47. A Bonus Measure

4 48. Log-sine Integrals48. Sasaki’s Mahler Measures51. Log-sine-cosine integrals56. Three Cognate Evaluations58. KLO’s Mahler Measures62. Conclusion

J.M. Borwein Mahler Measures

Page 3: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

Math computation is hybrid: SNaG (symbolic-numeric & graphic)

1968 A ‘solved‘ MAA Monthly problem.1971 Withdrawn (formal manipulation ofnon absolute integral).2011 Appears still to be ‘open’?(according to JSTOR)Now a fine and doable symbolic/numericchallenge.

J.M. Borwein Mahler Measures

Page 4: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

Math computation is hybrid: SNaG (symbolic-numeric & graphic)

1968 A ‘solved‘ MAA Monthly problem.1971 Withdrawn (formal manipulation ofnon absolute integral).2011 Appears still to be ‘open’?(according to JSTOR)Now a fine and doable symbolic/numericchallenge.

J.M. Borwein Mahler Measures

Page 5: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

Math computation is hybrid: SNaG (symbolic-numeric & graphic)

1968 A ‘solved‘ MAA Monthly problem.1971 Withdrawn (formal manipulation ofnon absolute integral).2011 Appears still to be ‘open’?(according to JSTOR)Now a fine and doable symbolic/numericchallenge.

J.M. Borwein Mahler Measures

Page 6: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

Some points I will be making.I hope to illustrate:

• Why SNaG is important for mathematics• Why SNaG needs mathematical consumers and testers

I wish to emphasise about mathematicians using SNaG to domathematics that:

1 They are often too trusting of answers

2 They are often too suspicious of failure

3 They need robust generic code not best practice methods

4 They are naive outside of their own domain

In the first place, the beginner [us most of the time] mustbe convinced that proofs deserve to be studied, that theyhave a purpose, that they are interesting. — GeorgePolya (1887-1985)

J.M. Borwein Mahler Measures

Page 7: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

Some points I will be making.I hope to illustrate:

• Why SNaG is important for mathematics• Why SNaG needs mathematical consumers and testers

I wish to emphasise about mathematicians using SNaG to domathematics that:

1 They are often too trusting of answers

2 They are often too suspicious of failure

3 They need robust generic code not best practice methods

4 They are naive outside of their own domain

In the first place, the beginner [us most of the time] mustbe convinced that proofs deserve to be studied, that theyhave a purpose, that they are interesting. — GeorgePolya (1887-1985)

J.M. Borwein Mahler Measures

Page 8: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

Some points I will be making.I hope to illustrate:

• Why SNaG is important for mathematics• Why SNaG needs mathematical consumers and testers

I wish to emphasise about mathematicians using SNaG to domathematics that:

1 They are often too trusting of answers

2 They are often too suspicious of failure

3 They need robust generic code not best practice methods

4 They are naive outside of their own domain

In the first place, the beginner [us most of the time] mustbe convinced that proofs deserve to be studied, that theyhave a purpose, that they are interesting. — GeorgePolya (1887-1985)

J.M. Borwein Mahler Measures

Page 9: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

Some points I will be making.I hope to illustrate:

• Why SNaG is important for mathematics• Why SNaG needs mathematical consumers and testers

I wish to emphasise about mathematicians using SNaG to domathematics that:

1 They are often too trusting of answers

2 They are often too suspicious of failure

3 They need robust generic code not best practice methods

4 They are naive outside of their own domain

In the first place, the beginner [us most of the time] mustbe convinced that proofs deserve to be studied, that theyhave a purpose, that they are interesting. — GeorgePolya (1887-1985)

J.M. Borwein Mahler Measures

Page 10: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Congrats to NIST: for helping answer eternal questions

Special Functions in the 21st Century:

Theory & Applications April 6–8, 2011Washington, DC

Objectives. The conference will provide a forum for the exchange of expertise, experience and insights among world leaders in the subject of special functions. Participants will include expert authors, editors and validators of the recently published NIST Handbook of Mathematical Functions and Digital Library of Mathematical Functions (DLMF). It will also provide an opportunity for DLMF users to interact with its creators and to explore potential areas of fruitful future developments.

F.W.J. Olver

Special Recognition of Professor Frank W. J. Olver. This conference is dedicated to Professor Olver in light of his seminal con-tributions to the advancement of special functions, especially in the area of asymptotic analysis and as Mathematics Editor of the DLMF.

Plenary Speakers Richard Askey, University of Wisconsin Michael Berry, University of Bristol Nalini Joshi, University of Sydney, Australia Leonard Maximon, George Washington University William Reinhardt, University of Washington Roderick Wong, City University of Hong Kong

Call for Contributed Talks (25 Minutes) Abstracts may be submitted to [email protected] until March 15, 2011.

Registration and Financial Assistance. Registration fee: $120. Courtesy of SIAM, limited travel support is available for US-based postdoc and early career researchers. Courtesy of City University of Hong Kong and NIST, partial support is available for others in cases of need. Submit all requests for financial assistance to [email protected].

Venue. Renaissance Washington Dupont Circle Hotel, 1143 New Hampshire Avenue NW, Washington, DC, 20037 USA. The conference rate is $259, available until March 15. Refreshments are supplied courtesy of University of Maryland.

Organizing Committee. Daniel Lozier, NIST, Gaithersburg, Maryland; Adri Olde Daalhuis, Univer-sity of Edinburgh; Nico Temme, CWI, Amsterdam; Roderick Wong, City University of Hong Kong

To register online for the conference, and reserve a room at the conference hotel, see http://math.nist.gov/~DLozier/SF21

J.M. Borwein Mahler Measures

Page 11: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Abstract toc

• The Mahler measure of a polynomial of several variables hasbeen a subject of much study over the past thirty years.

• Very few closed forms are proven but more are conjectured.

• We provide systematic evaluations of various higher andmultiple Mahler measures using moments of random walksand values of log-sine integrals.

• We also explore related generating functions for the log-sineintegrals and their generalizations.

• This work would be impossible without very extensive symbolicand numeric computations. It also makes frequent use of thenew NIST Handbook of Mathematical Functions.

I shall show the interplay between numeric and symboliccomputing while exploring the three mathematical topics in mytitle. (#4. also in http://arminstraub.com/talks.)

J.M. Borwein Mahler Measures

Page 12: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Abstract toc

• The Mahler measure of a polynomial of several variables hasbeen a subject of much study over the past thirty years.

• Very few closed forms are proven but more are conjectured.

• We provide systematic evaluations of various higher andmultiple Mahler measures using moments of random walksand values of log-sine integrals.

• We also explore related generating functions for the log-sineintegrals and their generalizations.

• This work would be impossible without very extensive symbolicand numeric computations. It also makes frequent use of thenew NIST Handbook of Mathematical Functions.

I shall show the interplay between numeric and symboliccomputing while exploring the three mathematical topics in mytitle. (#4. also in http://arminstraub.com/talks.)

J.M. Borwein Mahler Measures

Page 13: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Abstract toc

• The Mahler measure of a polynomial of several variables hasbeen a subject of much study over the past thirty years.

• Very few closed forms are proven but more are conjectured.

• We provide systematic evaluations of various higher andmultiple Mahler measures using moments of random walksand values of log-sine integrals.

• We also explore related generating functions for the log-sineintegrals and their generalizations.

• This work would be impossible without very extensive symbolicand numeric computations. It also makes frequent use of thenew NIST Handbook of Mathematical Functions.

I shall show the interplay between numeric and symboliccomputing while exploring the three mathematical topics in mytitle. (#4. also in http://arminstraub.com/talks.)

J.M. Borwein Mahler Measures

Page 14: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

.

J.M. Borwein Mahler Measures

Page 15: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Other References

1 Joint with: Armin Straub (Tulane) and James Wan (UofN)- and variously with: David Bailey (LBNL), David Borwein

(UWO), Dirk Nuyens (Leuven), Wadim Zudilin (UofN).

2 Most results are written up in FPSAC 2010, ISSAC 2011(JB-AS: best student paper),RAMA, Exp. Math, J. AustMS,Can. Math J. . See:

• www.carma.newcastle.edu.au/~jb616/walks.pdf• www.carma.newcastle.edu.au/~jb616/walks2.pdf• www.carma.newcastle.edu.au/~jb616/densities.pdf• www.carma.newcastle.edu.au/~jb616/logsin.pdf• www.carma.newcastle.edu.au/~jb616/logsin2.pdf.• http://carma.newcastle.edu.au/jon/logsin3.pdf

3 This and related talks are housed at www.carma.newcastle.edu.au/~jb616/papers.html#TALKS

J.M. Borwein Mahler Measures

Page 16: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Other References

1 Joint with: Armin Straub (Tulane) and James Wan (UofN)- and variously with: David Bailey (LBNL), David Borwein

(UWO), Dirk Nuyens (Leuven), Wadim Zudilin (UofN).

2 Most results are written up in FPSAC 2010, ISSAC 2011(JB-AS: best student paper),RAMA, Exp. Math, J. AustMS,Can. Math J. . See:

• www.carma.newcastle.edu.au/~jb616/walks.pdf• www.carma.newcastle.edu.au/~jb616/walks2.pdf• www.carma.newcastle.edu.au/~jb616/densities.pdf• www.carma.newcastle.edu.au/~jb616/logsin.pdf• www.carma.newcastle.edu.au/~jb616/logsin2.pdf.• http://carma.newcastle.edu.au/jon/logsin3.pdf

3 This and related talks are housed at www.carma.newcastle.edu.au/~jb616/papers.html#TALKS

J.M. Borwein Mahler Measures

Page 17: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Other References

1 Joint with: Armin Straub (Tulane) and James Wan (UofN)- and variously with: David Bailey (LBNL), David Borwein

(UWO), Dirk Nuyens (Leuven), Wadim Zudilin (UofN).

2 Most results are written up in FPSAC 2010, ISSAC 2011(JB-AS: best student paper),RAMA, Exp. Math, J. AustMS,Can. Math J. . See:

• www.carma.newcastle.edu.au/~jb616/walks.pdf• www.carma.newcastle.edu.au/~jb616/walks2.pdf• www.carma.newcastle.edu.au/~jb616/densities.pdf• www.carma.newcastle.edu.au/~jb616/logsin.pdf• www.carma.newcastle.edu.au/~jb616/logsin2.pdf.• http://carma.newcastle.edu.au/jon/logsin3.pdf

3 This and related talks are housed at www.carma.newcastle.edu.au/~jb616/papers.html#TALKS

J.M. Borwein Mahler Measures

Page 18: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

My Collaborators

J.M. Borwein Mahler Measures

Page 19: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Multiple Polylogarithms:

Lia1,...,ak(z) :=∑

n1>···>nk>0

zn1

na11 · · ·nakk

.

Thus, Li2,1(z) =∑∞

k=1zk

k2∑k−1

j=11j . Specializing produces:

• The polylogarithm of order k: Lik(x) =∑∞

n=1xn

nk.

• Multiple zeta values:

ζ(a1, . . . , ak) := Lia1,...,ak(1).

• Multiple Clausen (Cl) and Glaisher functions (Gl) of depth kand weight w :=

∑aj :

Cla1,...,ak (θ) :=

{Im Lia1,...,ak(eiθ) if w evenRe Lia1,...,ak(eiθ) if w odd

},

Gla1,...,ak (θ) :=

{Re Lia1,...,ak(eiθ) if w evenIm Lia1,...,ak(eiθ) if w odd

}.

J.M. Borwein Mahler Measures

Page 20: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Multiple Polylogarithms:

Lia1,...,ak(z) :=∑

n1>···>nk>0

zn1

na11 · · ·nakk

.

Thus, Li2,1(z) =∑∞

k=1zk

k2∑k−1

j=11j . Specializing produces:

• The polylogarithm of order k: Lik(x) =∑∞

n=1xn

nk.

• Multiple zeta values:

ζ(a1, . . . , ak) := Lia1,...,ak(1).

• Multiple Clausen (Cl) and Glaisher functions (Gl) of depth kand weight w :=

∑aj :

Cla1,...,ak (θ) :=

{Im Lia1,...,ak(eiθ) if w evenRe Lia1,...,ak(eiθ) if w odd

},

Gla1,...,ak (θ) :=

{Re Lia1,...,ak(eiθ) if w evenIm Lia1,...,ak(eiθ) if w odd

}.

J.M. Borwein Mahler Measures

Page 21: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Multiple Polylogarithms:

Lia1,...,ak(z) :=∑

n1>···>nk>0

zn1

na11 · · ·nakk

.

Thus, Li2,1(z) =∑∞

k=1zk

k2∑k−1

j=11j . Specializing produces:

• The polylogarithm of order k: Lik(x) =∑∞

n=1xn

nk.

• Multiple zeta values:

ζ(a1, . . . , ak) := Lia1,...,ak(1).

• Multiple Clausen (Cl) and Glaisher functions (Gl) of depth kand weight w :=

∑aj :

Cla1,...,ak (θ) :=

{Im Lia1,...,ak(eiθ) if w evenRe Lia1,...,ak(eiθ) if w odd

},

Gla1,...,ak (θ) :=

{Re Lia1,...,ak(eiθ) if w evenIm Lia1,...,ak(eiθ) if w odd

}.

J.M. Borwein Mahler Measures

Page 22: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Multiple Polylogarithms:

Lia1,...,ak(z) :=∑

n1>···>nk>0

zn1

na11 · · ·nakk

.

Thus, Li2,1(z) =∑∞

k=1zk

k2∑k−1

j=11j . Specializing produces:

• The polylogarithm of order k: Lik(x) =∑∞

n=1xn

nk.

• Multiple zeta values:

ζ(a1, . . . , ak) := Lia1,...,ak(1).

• Multiple Clausen (Cl) and Glaisher functions (Gl) of depth kand weight w :=

∑aj :

Cla1,...,ak (θ) :=

{Im Lia1,...,ak(eiθ) if w evenRe Lia1,...,ak(eiθ) if w odd

},

Gla1,...,ak (θ) :=

{Re Lia1,...,ak(eiθ) if w evenIm Lia1,...,ak(eiθ) if w odd

}.

J.M. Borwein Mahler Measures

Page 23: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Log-sine Integrals

The log-sine integrals are defined for n = 1, 2, . . . by

Lsn (σ) := −∫ σ

0logn−1

∣∣∣∣2 sinθ

2

∣∣∣∣ dθ (1)

and their moments for k ≥ 0 given by

Ls(k)n (σ) := −

∫ σ

0θk logn−1−k

∣∣∣∣2 sinθ

2

∣∣∣∣ dθ. (2)

• Ls1 (σ) = −σ and Ls(0)n (σ) = Lsn (σ), as in Lewin. In

particular,

Ls2 (σ) = Cl2 (σ) :=

∞∑n=1

sin(nσ)

n2(3)

is the Clausen function which plays a prominent role.

J.M. Borwein Mahler Measures

Page 24: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Log-sine Integrals

The log-sine integrals are defined for n = 1, 2, . . . by

Lsn (σ) := −∫ σ

0logn−1

∣∣∣∣2 sinθ

2

∣∣∣∣ dθ (1)

and their moments for k ≥ 0 given by

Ls(k)n (σ) := −

∫ σ

0θk logn−1−k

∣∣∣∣2 sinθ

2

∣∣∣∣ dθ. (2)

• Ls1 (σ) = −σ and Ls(0)n (σ) = Lsn (σ), as in Lewin. In

particular,

Ls2 (σ) = Cl2 (σ) :=

∞∑n=1

sin(nσ)

n2(3)

is the Clausen function which plays a prominent role.

J.M. Borwein Mahler Measures

Page 25: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Moments of Uniform Random Walks

Definition (Moments)

For complex s the n-th moment function is

Wn(s) :=

∫[0,1]n

∣∣∣∣∣n∑k=1

e2πxki

∣∣∣∣∣s

dx

Thus, Wn := Wn(1) is the expectation.

• The integral for Wn is analytic precisely for Re s > −2.

1905. Originated with Pearson, and Raleigh:

“What is probability at time n that the rambler is withinone unit of home?”

J.M. Borwein Mahler Measures

Page 26: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Moments of Uniform Random Walks

Definition (Moments)

For complex s the n-th moment function is

Wn(s) :=

∫[0,1]n

∣∣∣∣∣n∑k=1

e2πxki

∣∣∣∣∣s

dx

Thus, Wn := Wn(1) is the expectation.

• The integral for Wn is analytic precisely for Re s > −2.

1905. Originated with Pearson, and Raleigh:

“What is probability at time n that the rambler is withinone unit of home?”

J.M. Borwein Mahler Measures

Page 27: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Moments of Uniform Random Walks

Definition (Moments)

For complex s the n-th moment function is

Wn(s) :=

∫[0,1]n

∣∣∣∣∣n∑k=1

e2πxki

∣∣∣∣∣s

dx

Thus, Wn := Wn(1) is the expectation.

• The integral for Wn is analytic precisely for Re s > −2.

1905. Originated with Pearson, and Raleigh:

“What is probability at time n that the rambler is withinone unit of home?”

J.M. Borwein Mahler Measures

Page 28: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Moments of Uniform Random Walks

Definition (Moments)

For complex s the n-th moment function is

Wn(s) :=

∫[0,1]n

∣∣∣∣∣n∑k=1

e2πxki

∣∣∣∣∣s

dx

Thus, Wn := Wn(1) is the expectation.

• The integral for Wn is analytic precisely for Re s > −2.

1905. Originated with Pearson, and Raleigh:

“What is probability at time n that the rambler is withinone unit of home?”

J.M. Borwein Mahler Measures

Page 29: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Clearly W1 = 1. What about W2(1)?

W2 =

∫ 1

0

∫ 1

0

∣∣e2πix + e2πiy∣∣ dxdy = ?

– Mathematica 7 and Maple 14 think the answer is 0.

• There is always a 1-dimension reduction

Wn(s) =

∫[0,1]n

∣∣∣∣ n∑k=1

e2πxki

∣∣∣∣sdx=

∫[0,1]n−1

∣∣∣∣1 +n−1∑k=1

e2πxki

∣∣∣∣sd(x1, . . . , xn−1)

• So

W2 = 4

∫ 1/4

0cos(πx) dx =

4

π.

J.M. Borwein Mahler Measures

Page 30: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Clearly W1 = 1. What about W2(1)?

W2 =

∫ 1

0

∫ 1

0

∣∣e2πix + e2πiy∣∣ dxdy = ?

– Mathematica 7 and Maple 14 think the answer is 0.

• There is always a 1-dimension reduction

Wn(s) =

∫[0,1]n

∣∣∣∣ n∑k=1

e2πxki

∣∣∣∣sdx=

∫[0,1]n−1

∣∣∣∣1 +

n−1∑k=1

e2πxki

∣∣∣∣sd(x1, . . . , xn−1)

• So

W2 = 4

∫ 1/4

0cos(πx) dx =

4

π.

J.M. Borwein Mahler Measures

Page 31: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Clearly W1 = 1. What about W2(1)?

W2 =

∫ 1

0

∫ 1

0

∣∣e2πix + e2πiy∣∣ dxdy = ?

– Mathematica 7 and Maple 14 think the answer is 0.

• There is always a 1-dimension reduction

Wn(s) =

∫[0,1]n

∣∣∣∣ n∑k=1

e2πxki

∣∣∣∣sdx=

∫[0,1]n−1

∣∣∣∣1 +

n−1∑k=1

e2πxki

∣∣∣∣sd(x1, . . . , xn−1)

• So

W2 = 4

∫ 1/4

0cos(πx) dx =

4

π.

J.M. Borwein Mahler Measures

Page 32: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Clearly W1 = 1. What about W2(1)?

W2 =

∫ 1

0

∫ 1

0

∣∣e2πix + e2πiy∣∣ dxdy = ?

– Mathematica 7 and Maple 14 think the answer is 0.

• There is always a 1-dimension reduction

Wn(s) =

∫[0,1]n

∣∣∣∣ n∑k=1

e2πxki

∣∣∣∣sdx=

∫[0,1]n−1

∣∣∣∣1 +

n−1∑k=1

e2πxki

∣∣∣∣sd(x1, . . . , xn−1)

• So

W2 = 4

∫ 1/4

0cos(πx) dx =

4

π.

J.M. Borwein Mahler Measures

Page 33: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

n ≥ 3 highly nontrivial and n ≥ 5 not well understood.

• Similar problems get much more difficult in five or moredimensions — e.g., Bessel moments, Box integrals, Isingintegrals (work with Bailey, Broadhurst, Crandall, ...).

- In fact, W5 ≈ 2.0081618 was the best estimate we couldcompute directly, on 256 cores at Lawrence Berkeley NationalLaboratory.

- Bailey and I have a general project to develop symbolicnumeric techniques for (meaningful) multi-dim integrals.

When the facts change, I change my mind. What do you do, sir?— John Maynard Keynes in Economist Dec 18, 1999.

J.M. Borwein Mahler Measures

Page 34: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

n ≥ 3 highly nontrivial and n ≥ 5 not well understood.

• Similar problems get much more difficult in five or moredimensions — e.g., Bessel moments, Box integrals, Isingintegrals (work with Bailey, Broadhurst, Crandall, ...).

- In fact, W5 ≈ 2.0081618 was the best estimate we couldcompute directly, on 256 cores at Lawrence Berkeley NationalLaboratory.

- Bailey and I have a general project to develop symbolicnumeric techniques for (meaningful) multi-dim integrals.

When the facts change, I change my mind. What do you do, sir?— John Maynard Keynes in Economist Dec 18, 1999.

J.M. Borwein Mahler Measures

Page 35: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

n ≥ 3 highly nontrivial and n ≥ 5 not well understood.

• Similar problems get much more difficult in five or moredimensions — e.g., Bessel moments, Box integrals, Isingintegrals (work with Bailey, Broadhurst, Crandall, ...).

- In fact, W5 ≈ 2.0081618 was the best estimate we couldcompute directly, on 256 cores at Lawrence Berkeley NationalLaboratory.

- Bailey and I have a general project to develop symbolicnumeric techniques for (meaningful) multi-dim integrals.

When the facts change, I change my mind. What do you do, sir?— John Maynard Keynes in Economist Dec 18, 1999.

J.M. Borwein Mahler Measures

Page 36: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

n ≥ 3 highly nontrivial and n ≥ 5 not well understood.

• Similar problems get much more difficult in five or moredimensions — e.g., Bessel moments, Box integrals, Isingintegrals (work with Bailey, Broadhurst, Crandall, ...).

- In fact, W5 ≈ 2.0081618 was the best estimate we couldcompute directly, on 256 cores at Lawrence Berkeley NationalLaboratory.

- Bailey and I have a general project to develop symbolicnumeric techniques for (meaningful) multi-dim integrals.

When the facts change, I change my mind. What do you do, sir?— John Maynard Keynes in Economist Dec 18, 1999.

J.M. Borwein Mahler Measures

Page 37: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

One 1500-step Ramble: a familiar picture

2D and 3D lattice walks are

different:

A drunk man willfind his wayhome but adrunk bird mayget lost forever.— ShizuoKakutani

• 1D (and 3D) easy. Expectation of RMS distance is easy (√n).

• 1D or 2D lattice: probability one of returning to the origin.

J.M. Borwein Mahler Measures

Page 38: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

One 1500-step Ramble: a familiar picture

2D and 3D lattice walks are

different:

A drunk man willfind his wayhome but adrunk bird mayget lost forever.— ShizuoKakutani

• 1D (and 3D) easy. Expectation of RMS distance is easy (√n).

• 1D or 2D lattice: probability one of returning to the origin.

J.M. Borwein Mahler Measures

Page 39: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

One 1500-step Ramble: a familiar picture

2D and 3D lattice walks are

different:

A drunk man willfind his wayhome but adrunk bird mayget lost forever.— ShizuoKakutani

• 1D (and 3D) easy. Expectation of RMS distance is easy (√n).

• 1D or 2D lattice: probability one of returning to the origin.

J.M. Borwein Mahler Measures

Page 40: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

1000 three-step Rambles: a less familiar picture?

J.M. Borwein Mahler Measures

Page 41: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Mahler Measures (1923) in several variables

The logarithmic Mahler measure of a (Laurent) polynomial P :

µ(P ) :=

∫ 1

0

∫ 1

0· · ·∫ 1

0log |P

(e2πiθ1 , · · · , e2πiθn

)| dθ1 · · · dθn.

• M1 := P 7→ exp(µ(P )) is multiplicative.• n = 1: P is a product of cyclotomics ⇔M1(P ) = 1.

Lehmer’s conjecture (1931) is: otherwiseM1(P ) ≥M1(1− x+ x3 − x4 + x5 − x6 + x7 − x9 + x10).

• µ(P ) turns out to be an example of a period.• When n = 1 and P has integer coefficients M1(P ) is an

algebraic integer.• In several dimensions life is harder.

- We shall see remarkable recent results — many morediscovered than proven — expressing µ(P ) arithmetically.

J.M. Borwein Mahler Measures

Page 42: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Mahler Measures (1923) in several variables

The logarithmic Mahler measure of a (Laurent) polynomial P :

µ(P ) :=

∫ 1

0

∫ 1

0· · ·∫ 1

0log |P

(e2πiθ1 , · · · , e2πiθn

)| dθ1 · · · dθn.

• M1 := P 7→ exp(µ(P )) is multiplicative.• n = 1: P is a product of cyclotomics ⇔M1(P ) = 1.

Lehmer’s conjecture (1931) is: otherwiseM1(P ) ≥M1(1− x+ x3 − x4 + x5 − x6 + x7 − x9 + x10).

• µ(P ) turns out to be an example of a period.• When n = 1 and P has integer coefficients M1(P ) is an

algebraic integer.• In several dimensions life is harder.

- We shall see remarkable recent results — many morediscovered than proven — expressing µ(P ) arithmetically.

J.M. Borwein Mahler Measures

Page 43: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Mahler Measures (1923) in several variables

The logarithmic Mahler measure of a (Laurent) polynomial P :

µ(P ) :=

∫ 1

0

∫ 1

0· · ·∫ 1

0log |P

(e2πiθ1 , · · · , e2πiθn

)| dθ1 · · · dθn.

• M1 := P 7→ exp(µ(P )) is multiplicative.• n = 1: P is a product of cyclotomics ⇔M1(P ) = 1.

Lehmer’s conjecture (1931) is: otherwiseM1(P ) ≥M1(1− x+ x3 − x4 + x5 − x6 + x7 − x9 + x10).

• µ(P ) turns out to be an example of a period.• When n = 1 and P has integer coefficients M1(P ) is an

algebraic integer.• In several dimensions life is harder.

- We shall see remarkable recent results — many morediscovered than proven — expressing µ(P ) arithmetically.

J.M. Borwein Mahler Measures

Page 44: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Mahler Measures (1923) in several variables

The logarithmic Mahler measure of a (Laurent) polynomial P :

µ(P ) :=

∫ 1

0

∫ 1

0· · ·∫ 1

0log |P

(e2πiθ1 , · · · , e2πiθn

)| dθ1 · · · dθn.

• M1 := P 7→ exp(µ(P )) is multiplicative.• n = 1: P is a product of cyclotomics ⇔M1(P ) = 1.

Lehmer’s conjecture (1931) is: otherwiseM1(P ) ≥M1(1− x+ x3 − x4 + x5 − x6 + x7 − x9 + x10).

• µ(P ) turns out to be an example of a period.• When n = 1 and P has integer coefficients M1(P ) is an

algebraic integer.• In several dimensions life is harder.

- We shall see remarkable recent results — many morediscovered than proven — expressing µ(P ) arithmetically.

J.M. Borwein Mahler Measures

Page 45: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Mahler Measures (1923) in several variables

The logarithmic Mahler measure of a (Laurent) polynomial P :

µ(P ) :=

∫ 1

0

∫ 1

0· · ·∫ 1

0log |P

(e2πiθ1 , · · · , e2πiθn

)| dθ1 · · · dθn.

• M1 := P 7→ exp(µ(P )) is multiplicative.• n = 1: P is a product of cyclotomics ⇔M1(P ) = 1.

Lehmer’s conjecture (1931) is: otherwiseM1(P ) ≥M1(1− x+ x3 − x4 + x5 − x6 + x7 − x9 + x10).

• µ(P ) turns out to be an example of a period.• When n = 1 and P has integer coefficients M1(P ) is an

algebraic integer.• In several dimensions life is harder.

- We shall see remarkable recent results — many morediscovered than proven — expressing µ(P ) arithmetically.

J.M. Borwein Mahler Measures

Page 46: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Mahler Measures (1923) in several variables

The logarithmic Mahler measure of a (Laurent) polynomial P :

µ(P ) :=

∫ 1

0

∫ 1

0· · ·∫ 1

0log |P

(e2πiθ1 , · · · , e2πiθn

)| dθ1 · · · dθn.

• M1 := P 7→ exp(µ(P )) is multiplicative.• n = 1: P is a product of cyclotomics ⇔M1(P ) = 1.

Lehmer’s conjecture (1931) is: otherwiseM1(P ) ≥M1(1− x+ x3 − x4 + x5 − x6 + x7 − x9 + x10).

• µ(P ) turns out to be an example of a period.• When n = 1 and P has integer coefficients M1(P ) is an

algebraic integer.• In several dimensions life is harder.

- We shall see remarkable recent results — many morediscovered than proven — expressing µ(P ) arithmetically.

J.M. Borwein Mahler Measures

Page 47: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Carlson’s Theorem: from discrete to continuous

Theorem (Carlson (1914, PhD) )

If f(z) is analytic for Re (z) ≥ 0, its growth on the imaginary axisis bounded by ecy, |c| < π, and

0 = f(0) = f(1) = f(2) = . . .

then f(z) = 0 identically.

• sin(πz) does not satisfy the conditions of the theorem, as itgrows like eπy on the imaginary axis.

• Wn(s) satisfies the conditions of the theorem (and is in factanalytic for Re (s) > −2 when n > 2).

• There is a lovely 1941 proof by Selberg of the bounded case.• The theorem lies under much of what follows.

J.M. Borwein Mahler Measures

Page 48: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Carlson’s Theorem: from discrete to continuous

Theorem (Carlson (1914, PhD) )

If f(z) is analytic for Re (z) ≥ 0, its growth on the imaginary axisis bounded by ecy, |c| < π, and

0 = f(0) = f(1) = f(2) = . . .

then f(z) = 0 identically.

• sin(πz) does not satisfy the conditions of the theorem, as itgrows like eπy on the imaginary axis.

• Wn(s) satisfies the conditions of the theorem (and is in factanalytic for Re (s) > −2 when n > 2).

• There is a lovely 1941 proof by Selberg of the bounded case.• The theorem lies under much of what follows.

J.M. Borwein Mahler Measures

Page 49: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Carlson’s Theorem: from discrete to continuous

Theorem (Carlson (1914, PhD) )

If f(z) is analytic for Re (z) ≥ 0, its growth on the imaginary axisis bounded by ecy, |c| < π, and

0 = f(0) = f(1) = f(2) = . . .

then f(z) = 0 identically.

• sin(πz) does not satisfy the conditions of the theorem, as itgrows like eπy on the imaginary axis.

• Wn(s) satisfies the conditions of the theorem (and is in factanalytic for Re (s) > −2 when n > 2).

• There is a lovely 1941 proof by Selberg of the bounded case.• The theorem lies under much of what follows.

J.M. Borwein Mahler Measures

Page 50: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

9. Multiple Polylogarithms10. Log-sine Integrals11. Random Walks16. Mahler Measures17. Carlson’s Theorem

Carlson’s Theorem: from discrete to continuous

Theorem (Carlson (1914, PhD) )

If f(z) is analytic for Re (z) ≥ 0, its growth on the imaginary axisis bounded by ecy, |c| < π, and

0 = f(0) = f(1) = f(2) = . . .

then f(z) = 0 identically.

• sin(πz) does not satisfy the conditions of the theorem, as itgrows like eπy on the imaginary axis.

• Wn(s) satisfies the conditions of the theorem (and is in factanalytic for Re (s) > −2 when n > 2).

• There is a lovely 1941 proof by Selberg of the bounded case.• The theorem lies under much of what follows.

J.M. Borwein Mahler Measures

Page 51: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

A Little History: from a vast literature toc

L: Pearson posed question(Nature, 1905).

R: Rayleigh gave large n asymptotics:pn(x) ∼ 2x

n e−x2/n (Nature, 1905).

John William Strutt (Lord Rayleigh) (1842-1919): discoverer ofArgon, explained why sky is blue.

The problem “is the same as that of the composition of n isoperiodicvibrations of unit amplitude and phases distributed at random” he studiedin 1880 (diffusion eq’n, Brownian motion, ...)

Karl Pearson (1857-1936): founded statistics, eugenicist & socialist,changed name (C 7→ K), declined knighthood.

- UNSW: Donovan and Nuyens, WWII cryptography.

- Appear in quantum chemistry, in quantum physics as hexagonal and diamond lattice integers, etc ...

J.M. Borwein Mahler Measures

Page 52: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

A Little History: from a vast literature toc

L: Pearson posed question(Nature, 1905).

R: Rayleigh gave large n asymptotics:pn(x) ∼ 2x

n e−x2/n (Nature, 1905).

John William Strutt (Lord Rayleigh) (1842-1919): discoverer ofArgon, explained why sky is blue.

The problem “is the same as that of the composition of n isoperiodicvibrations of unit amplitude and phases distributed at random” he studiedin 1880 (diffusion eq’n, Brownian motion, ...)

Karl Pearson (1857-1936): founded statistics, eugenicist & socialist,changed name (C 7→ K), declined knighthood.

- UNSW: Donovan and Nuyens, WWII cryptography.

- Appear in quantum chemistry, in quantum physics as hexagonal and diamond lattice integers, etc ...

J.M. Borwein Mahler Measures

Page 53: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

A Little History: from a vast literature toc

L: Pearson posed question(Nature, 1905).

R: Rayleigh gave large n asymptotics:pn(x) ∼ 2x

n e−x2/n (Nature, 1905).

John William Strutt (Lord Rayleigh) (1842-1919): discoverer ofArgon, explained why sky is blue.

The problem “is the same as that of the composition of n isoperiodicvibrations of unit amplitude and phases distributed at random” he studiedin 1880 (diffusion eq’n, Brownian motion, ...)

Karl Pearson (1857-1936): founded statistics, eugenicist & socialist,changed name (C 7→ K), declined knighthood.

- UNSW: Donovan and Nuyens, WWII cryptography.

- Appear in quantum chemistry, in quantum physics as hexagonal and diamond lattice integers, etc ...

J.M. Borwein Mahler Measures

Page 54: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

A Little History: from a vast literature toc

L: Pearson posed question(Nature, 1905).

R: Rayleigh gave large n asymptotics:pn(x) ∼ 2x

n e−x2/n (Nature, 1905).

John William Strutt (Lord Rayleigh) (1842-1919): discoverer ofArgon, explained why sky is blue.

The problem “is the same as that of the composition of n isoperiodicvibrations of unit amplitude and phases distributed at random” he studiedin 1880 (diffusion eq’n, Brownian motion, ...)

Karl Pearson (1857-1936): founded statistics, eugenicist & socialist,changed name (C 7→ K), declined knighthood.

- UNSW: Donovan and Nuyens, WWII cryptography.

- Appear in quantum chemistry, in quantum physics as hexagonal and diamond lattice integers, etc ...

J.M. Borwein Mahler Measures

Page 55: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

A Little History: from a vast literature toc

L: Pearson posed question(Nature, 1905).

R: Rayleigh gave large n asymptotics:pn(x) ∼ 2x

n e−x2/n (Nature, 1905).

John William Strutt (Lord Rayleigh) (1842-1919): discoverer ofArgon, explained why sky is blue.

The problem “is the same as that of the composition of n isoperiodicvibrations of unit amplitude and phases distributed at random” he studiedin 1880 (diffusion eq’n, Brownian motion, ...)

Karl Pearson (1857-1936): founded statistics, eugenicist & socialist,changed name (C 7→ K), declined knighthood.

- UNSW: Donovan and Nuyens, WWII cryptography.

- Appear in quantum chemistry, in quantum physics as hexagonal and diamond lattice integers, etc ...

J.M. Borwein Mahler Measures

Page 56: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

A Little History: from a vast literature toc

L: Pearson posed question(Nature, 1905).

R: Rayleigh gave large n asymptotics:pn(x) ∼ 2x

n e−x2/n (Nature, 1905).

John William Strutt (Lord Rayleigh) (1842-1919): discoverer ofArgon, explained why sky is blue.

The problem “is the same as that of the composition of n isoperiodicvibrations of unit amplitude and phases distributed at random” he studiedin 1880 (diffusion eq’n, Brownian motion, ...)

Karl Pearson (1857-1936): founded statistics, eugenicist & socialist,changed name (C 7→ K), declined knighthood.

- UNSW: Donovan and Nuyens, WWII cryptography.

- Appear in quantum chemistry, in quantum physics as hexagonal and diamond lattice integers, etc ...

J.M. Borwein Mahler Measures

Page 57: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Wn(k) at even values

Even values are easier (combinatorial – no square roots).

k 0 2 4 6 8 10

W2(k) 1 2 6 20 70 252

W3(k) 1 3 15 93 639 4653

W4(k) 1 4 28 256 2716 31504

W5(k) 1 5 45 545 7885 127905

• Can get started by rapidly computing many values naively assymbolic integrals.

• Observe that W2(s) =(ss/2

)for s > −1.

• Entering 1,5,45,545 in the OIES now gives “The functionW5(2n) (see Borwein et al. reference for definition).”

J.M. Borwein Mahler Measures

Page 58: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Wn(k) at even values

Even values are easier (combinatorial – no square roots).

k 0 2 4 6 8 10

W2(k) 1 2 6 20 70 252

W3(k) 1 3 15 93 639 4653

W4(k) 1 4 28 256 2716 31504

W5(k) 1 5 45 545 7885 127905

• Can get started by rapidly computing many values naively assymbolic integrals.

• Observe that W2(s) =(ss/2

)for s > −1.

• Entering 1,5,45,545 in the OIES now gives “The functionW5(2n) (see Borwein et al. reference for definition).”

J.M. Borwein Mahler Measures

Page 59: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Wn(k) at even values

Even values are easier (combinatorial – no square roots).

k 0 2 4 6 8 10

W2(k) 1 2 6 20 70 252

W3(k) 1 3 15 93 639 4653

W4(k) 1 4 28 256 2716 31504

W5(k) 1 5 45 545 7885 127905

• Can get started by rapidly computing many values naively assymbolic integrals.

• Observe that W2(s) =(ss/2

)for s > −1.

• Entering 1,5,45,545 in the OIES now gives “The functionW5(2n) (see Borwein et al. reference for definition).”

J.M. Borwein Mahler Measures

Page 60: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Wn(k) at even values

Even values are easier (combinatorial – no square roots).

k 0 2 4 6 8 10

W2(k) 1 2 6 20 70 252

W3(k) 1 3 15 93 639 4653

W4(k) 1 4 28 256 2716 31504

W5(k) 1 5 45 545 7885 127905

• Can get started by rapidly computing many values naively assymbolic integrals.

• Observe that W2(s) =(ss/2

)for s > −1.

• Entering 1,5,45,545 in the OIES now gives “The functionW5(2n) (see Borwein et al. reference for definition).”

J.M. Borwein Mahler Measures

Page 61: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Wn(k) at odd integers

n k = 1 k = 3 k = 5 k = 7 k = 9

2 1.27324 3.39531 10.8650 37.2514 132.449

3 1.57460 6.45168 36.7052 241.544 1714.62

4 1.79909 10.1207 82.6515 822.273 9169.625 2.00816 14.2896 152.316 2037.14 31393.1

6 2.19386 18.9133 248.759 4186.19 82718.9

Please, memorize this number!During the three years which I spent at Cambridge my time was wasted, as far as the academical studies were

concerned, as completely as at Edinburgh and at school. I attempted mathematics, and even went during the

summer of 1828 with a private tutor (a very dull man) to Barmouth, but I got on very slowly. The work was

repugnant to me, chiefly from my not being able to see any meaning in the early steps in algebra. This impatience

was very foolish, and in after years I have deeply regretted that I did not proceed far enough at least to understand

something of the great leading principles of mathematics, for men thus endowed seem to have an extra sense. —

Autobiography of Charles Darwin

J.M. Borwein Mahler Measures

Page 62: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Wn(k) at odd integers

n k = 1 k = 3 k = 5 k = 7 k = 9

2 1.27324 3.39531 10.8650 37.2514 132.449

3 1.57460 6.45168 36.7052 241.544 1714.62

4 1.79909 10.1207 82.6515 822.273 9169.625 2.00816 14.2896 152.316 2037.14 31393.1

6 2.19386 18.9133 248.759 4186.19 82718.9

Please, memorize this number!During the three years which I spent at Cambridge my time was wasted, as far as the academical studies were

concerned, as completely as at Edinburgh and at school. I attempted mathematics, and even went during the

summer of 1828 with a private tutor (a very dull man) to Barmouth, but I got on very slowly. The work was

repugnant to me, chiefly from my not being able to see any meaning in the early steps in algebra. This impatience

was very foolish, and in after years I have deeply regretted that I did not proceed far enough at least to understand

something of the great leading principles of mathematics, for men thus endowed seem to have an extra sense. —

Autobiography of Charles Darwin

J.M. Borwein Mahler Measures

Page 63: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Wn(k) at odd integers

n k = 1 k = 3 k = 5 k = 7 k = 9

2 1.27324 3.39531 10.8650 37.2514 132.449

3 1.57460 6.45168 36.7052 241.544 1714.62

4 1.79909 10.1207 82.6515 822.273 9169.625 2.00816 14.2896 152.316 2037.14 31393.1

6 2.19386 18.9133 248.759 4186.19 82718.9

Please, memorize this number!During the three years which I spent at Cambridge my time was wasted, as far as the academical studies were

concerned, as completely as at Edinburgh and at school. I attempted mathematics, and even went during the

summer of 1828 with a private tutor (a very dull man) to Barmouth, but I got on very slowly. The work was

repugnant to me, chiefly from my not being able to see any meaning in the early steps in algebra. This impatience

was very foolish, and in after years I have deeply regretted that I did not proceed far enough at least to understand

something of the great leading principles of mathematics, for men thus endowed seem to have an extra sense. —

Autobiography of Charles Darwin

J.M. Borwein Mahler Measures

Page 64: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Resolution at even values

• General even formula counts n-letter abelian squares xπ(x) oflength 2k.

– Shallit and Richmond (2008) give asymptotics:

Wn(2k) =∑

a1+...+an=k

(k

a1, ..., an

)2

. (4)

• Known to satisfy convolutions:

Wn1+n2(2k) =

k∑j=0

(k

j

)2

Wn1(2j)Wn2(2(k − j)).

• Has recursions such as:

(k + 2)2W3(2k + 4)− (10k2 + 30k + 23)W3(2k + 2)

+9(k + 1)2W3(2k) = 0.

J.M. Borwein Mahler Measures

Page 65: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Resolution at even values

• General even formula counts n-letter abelian squares xπ(x) oflength 2k.

– Shallit and Richmond (2008) give asymptotics:

Wn(2k) =∑

a1+...+an=k

(k

a1, ..., an

)2

. (4)

• Known to satisfy convolutions:

Wn1+n2(2k) =

k∑j=0

(k

j

)2

Wn1(2j)Wn2(2(k − j)).

• Has recursions such as:

(k + 2)2W3(2k + 4)− (10k2 + 30k + 23)W3(2k + 2)

+9(k + 1)2W3(2k) = 0.

J.M. Borwein Mahler Measures

Page 66: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Resolution at even values

• General even formula counts n-letter abelian squares xπ(x) oflength 2k.

– Shallit and Richmond (2008) give asymptotics:

Wn(2k) =∑

a1+...+an=k

(k

a1, ..., an

)2

. (4)

• Known to satisfy convolutions:

Wn1+n2(2k) =

k∑j=0

(k

j

)2

Wn1(2j)Wn2(2(k − j)).

• Has recursions such as:

(k + 2)2W3(2k + 4)− (10k2 + 30k + 23)W3(2k + 2)

+9(k + 1)2W3(2k) = 0.

J.M. Borwein Mahler Measures

Page 67: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Analytic continuation: From Carlson’s Theorem

• So integer recurrences yield complex functional equations. Viz

(s+4)2W3(s+4)−2(5s2+30s+46)W3(s+2)+9(s+2)2W3(s) = 0.

• This gives analytic continuations of the ramble integrals tothe complex plane, with poles at certain negative integers(likewise for all n).

– W3(s) has a simple pole at −2 with residue 2√3π, and other

simple poles at −2k with residues a rational multiple of Res−2.

“For it is easier to supply the proof when we have previously acquired, by

the method [of mechanical theorems], some knowledge of the questions

than it is to find it without any previous knowledge. — Archimedes.

J.M. Borwein Mahler Measures

Page 68: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Analytic continuation: From Carlson’s Theorem

• So integer recurrences yield complex functional equations. Viz

(s+4)2W3(s+4)−2(5s2+30s+46)W3(s+2)+9(s+2)2W3(s) = 0.

• This gives analytic continuations of the ramble integrals tothe complex plane, with poles at certain negative integers(likewise for all n).

– W3(s) has a simple pole at −2 with residue 2√3π, and other

simple poles at −2k with residues a rational multiple of Res−2.

“For it is easier to supply the proof when we have previously acquired, by

the method [of mechanical theorems], some knowledge of the questions

than it is to find it without any previous knowledge. — Archimedes.

J.M. Borwein Mahler Measures

Page 69: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Analytic continuation: From Carlson’s Theorem

• So integer recurrences yield complex functional equations. Viz

(s+4)2W3(s+4)−2(5s2+30s+46)W3(s+2)+9(s+2)2W3(s) = 0.

• This gives analytic continuations of the ramble integrals tothe complex plane, with poles at certain negative integers(likewise for all n).

– W3(s) has a simple pole at −2 with residue 2√3π, and other

simple poles at −2k with residues a rational multiple of Res−2.

“For it is easier to supply the proof when we have previously acquired, by

the method [of mechanical theorems], some knowledge of the questions

than it is to find it without any previous knowledge. — Archimedes.

J.M. Borwein Mahler Measures

Page 70: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Odd dimensions look like 3

W3(s) on [−6, 52 ]

• JW proved zeroes near to but not at integers: W3(−2n− 1) ↓ 0.J.M. Borwein Mahler Measures

Page 71: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Odd dimensions look like 3

W3(s) on [−6, 52 ]

• JW proved zeroes near to but not at integers: W3(−2n− 1) ↓ 0.J.M. Borwein Mahler Measures

Page 72: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Some even dimensions look more like 4

L: W4(s) on [−6, 1/2]. R: W5 on [−6, 2] (T), W6 on [−6, 2] (B).

• The functional equation (with double poles) for n = 4 is

(s+ 4)3W4(s+ 4) − 4(s+ 3)(5s2 + 30s+ 48)W4(s+ 2)

+ 64(s+ 2)3W4(s) = 0

• There are (infinitely many) multiple poles if and only if 4|n.• Why is W4 positive on R?

J.M. Borwein Mahler Measures

Page 73: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Some even dimensions look more like 4

L: W4(s) on [−6, 1/2]. R: W5 on [−6, 2] (T), W6 on [−6, 2] (B).

• The functional equation (with double poles) for n = 4 is

(s+ 4)3W4(s+ 4) − 4(s+ 3)(5s2 + 30s+ 48)W4(s+ 2)

+ 64(s+ 2)3W4(s) = 0

• There are (infinitely many) multiple poles if and only if 4|n.• Why is W4 positive on R?

J.M. Borwein Mahler Measures

Page 74: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Some even dimensions look more like 4

L: W4(s) on [−6, 1/2]. R: W5 on [−6, 2] (T), W6 on [−6, 2] (B).

• The functional equation (with double poles) for n = 4 is

(s+ 4)3W4(s+ 4) − 4(s+ 3)(5s2 + 30s+ 48)W4(s+ 2)

+ 64(s+ 2)3W4(s) = 0

• There are (infinitely many) multiple poles if and only if 4|n.• Why is W4 positive on R?

J.M. Borwein Mahler Measures

Page 75: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Some even dimensions look more like 4

L: W4(s) on [−6, 1/2]. R: W5 on [−6, 2] (T), W6 on [−6, 2] (B).

• The functional equation (with double poles) for n = 4 is

(s+ 4)3W4(s+ 4) − 4(s+ 3)(5s2 + 30s+ 48)W4(s+ 2)

+ 64(s+ 2)3W4(s) = 0

• There are (infinitely many) multiple poles if and only if 4|n.• Why is W4 positive on R?

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Meijer-G functions (1936– )

Definition

Gm,np,q

(a1, . . . , apb1, . . . , bq

∣∣∣∣x) :=1

2πi×

∫L

∏mj=1 Γ(bj − s)

∏nj=1 Γ(1− aj + s)∏p

j=n+1 Γ(aj − s)∏qj=m+1 Γ(1− bj + s)

xsds.

• Contour L lies between poles of Γ(1−ai− s) and of Γ(bi + s).

- A broad generalization of hypergeometric functions —capturing Bessel Y,K and much more.

- Important in CAS — if better hidden; often lead tosuperpositions of generalized hypergeometric terms pFq.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Meijer-G functions (1936– )

Definition

Gm,np,q

(a1, . . . , apb1, . . . , bq

∣∣∣∣x) :=1

2πi×

∫L

∏mj=1 Γ(bj − s)

∏nj=1 Γ(1− aj + s)∏p

j=n+1 Γ(aj − s)∏qj=m+1 Γ(1− bj + s)

xsds.

• Contour L lies between poles of Γ(1−ai− s) and of Γ(bi + s).

- A broad generalization of hypergeometric functions —capturing Bessel Y,K and much more.

- Important in CAS — if better hidden; often lead tosuperpositions of generalized hypergeometric terms pFq.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Meijer-G functions (1936– )

Definition

Gm,np,q

(a1, . . . , apb1, . . . , bq

∣∣∣∣x) :=1

2πi×

∫L

∏mj=1 Γ(bj − s)

∏nj=1 Γ(1− aj + s)∏p

j=n+1 Γ(aj − s)∏qj=m+1 Γ(1− bj + s)

xsds.

• Contour L lies between poles of Γ(1−ai− s) and of Γ(bi + s).

- A broad generalization of hypergeometric functions —capturing Bessel Y,K and much more.

- Important in CAS — if better hidden; often lead tosuperpositions of generalized hypergeometric terms pFq.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Meijer-G functions (1936– )

Definition

Gm,np,q

(a1, . . . , apb1, . . . , bq

∣∣∣∣x) :=1

2πi×

∫L

∏mj=1 Γ(bj − s)

∏nj=1 Γ(1− aj + s)∏p

j=n+1 Γ(aj − s)∏qj=m+1 Γ(1− bj + s)

xsds.

• Contour L lies between poles of Γ(1−ai− s) and of Γ(bi + s).

- A broad generalization of hypergeometric functions —capturing Bessel Y,K and much more.

- Important in CAS — if better hidden; often lead tosuperpositions of generalized hypergeometric terms pFq.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Meijer-G forms for W3

Theorem (Meijer form for W3)

For s not an odd integer

W3(s) =Γ(1 + s

2)√π Γ(− s

2)G21

33

(1, 1, 1

12 ,−

s2 ,−

s2

∣∣∣∣14).

• First found by Crandall via CAS.• Proved using residue calculus methods.• W3(s) is among few non-trivial Meijer-G with a closed form.

The most important aspect in solving a mathematical problem is the

conviction of what is the true result. Then it took 2 or 3 years using

the techniques that had been developed during the past 20 years or so.

— Lennart Carleson (From 1966 IMU address on his positive solution of

Luzin’s problem).J.M. Borwein Mahler Measures

Page 81: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Meijer-G forms for W3

Theorem (Meijer form for W3)

For s not an odd integer

W3(s) =Γ(1 + s

2)√π Γ(− s

2)G21

33

(1, 1, 1

12 ,−

s2 ,−

s2

∣∣∣∣14).

• First found by Crandall via CAS.• Proved using residue calculus methods.• W3(s) is among few non-trivial Meijer-G with a closed form.

The most important aspect in solving a mathematical problem is the

conviction of what is the true result. Then it took 2 or 3 years using

the techniques that had been developed during the past 20 years or so.

— Lennart Carleson (From 1966 IMU address on his positive solution of

Luzin’s problem).J.M. Borwein Mahler Measures

Page 82: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Meijer-G forms for W3

Theorem (Meijer form for W3)

For s not an odd integer

W3(s) =Γ(1 + s

2)√π Γ(− s

2)G21

33

(1, 1, 1

12 ,−

s2 ,−

s2

∣∣∣∣14).

• First found by Crandall via CAS.• Proved using residue calculus methods.• W3(s) is among few non-trivial Meijer-G with a closed form.

The most important aspect in solving a mathematical problem is the

conviction of what is the true result. Then it took 2 or 3 years using

the techniques that had been developed during the past 20 years or so.

— Lennart Carleson (From 1966 IMU address on his positive solution of

Luzin’s problem).J.M. Borwein Mahler Measures

Page 83: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Meijer-G forms for W3

Theorem (Meijer form for W3)

For s not an odd integer

W3(s) =Γ(1 + s

2)√π Γ(− s

2)G21

33

(1, 1, 1

12 ,−

s2 ,−

s2

∣∣∣∣14).

• First found by Crandall via CAS.• Proved using residue calculus methods.• W3(s) is among few non-trivial Meijer-G with a closed form.

The most important aspect in solving a mathematical problem is the

conviction of what is the true result. Then it took 2 or 3 years using

the techniques that had been developed during the past 20 years or so.

— Lennart Carleson (From 1966 IMU address on his positive solution of

Luzin’s problem).J.M. Borwein Mahler Measures

Page 84: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Meijer-G form for W4

Theorem (Meijer form for W4)

For Re s > −2 and s not an odd integer

W4(s) =2s

π

Γ(1 + s2)

Γ(− s2)

G2244

(1, 1−s

2 , 1, 112 −

s2 ,−

s2 ,−

s2

∣∣∣∣1). (5)

• Not helpful for odd integers. We must again look elsewhere ...

He [Gauss(or Mma)] is like the fox, who effaces his tracks in the sand with his tail.— Niels Abel (1802-1829)J.M. Borwein Mahler Measures

Page 85: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Meijer-G form for W4

Theorem (Meijer form for W4)

For Re s > −2 and s not an odd integer

W4(s) =2s

π

Γ(1 + s2)

Γ(− s2)

G2244

(1, 1−s

2 , 1, 112 −

s2 ,−

s2 ,−

s2

∣∣∣∣1). (5)

• Not helpful for odd integers. We must again look elsewhere ...

He [Gauss(or Mma)] is like the fox, who effaces his tracks in the sand with his tail.— Niels Abel (1802-1829)J.M. Borwein Mahler Measures

Page 86: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Meijer-G form for W4

Theorem (Meijer form for W4)

For Re s > −2 and s not an odd integer

W4(s) =2s

π

Γ(1 + s2)

Γ(− s2)

G2244

(1, 1−s

2 , 1, 112 −

s2 ,−

s2 ,−

s2

∣∣∣∣1). (5)

• Not helpful for odd integers. We must again look elsewhere ...

He [Gauss(or Mma)] is like the fox, who effaces his tracks in the sand with his tail.— Niels Abel (1802-1829)J.M. Borwein Mahler Measures

Page 87: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Meijer-G form for W4

Theorem (Meijer form for W4)

For Re s > −2 and s not an odd integer

W4(s) =2s

π

Γ(1 + s2)

Γ(− s2)

G2244

(1, 1−s

2 , 1, 112 −

s2 ,−

s2 ,−

s2

∣∣∣∣1). (5)

• Not helpful for odd integers. We must again look elsewhere ...

He [Gauss(or Mma)] is like the fox, who effaces his tracks in the sand with his tail.— Niels Abel (1802-1829)J.M. Borwein Mahler Measures

Page 88: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Meijer-G form for W4

Theorem (Meijer form for W4)

For Re s > −2 and s not an odd integer

W4(s) =2s

π

Γ(1 + s2)

Γ(− s2)

G2244

(1, 1−s

2 , 1, 112 −

s2 ,−

s2 ,−

s2

∣∣∣∣1). (5)

• Not helpful for odd integers. We must again look elsewhere ...

He [Gauss(or Mma)] is like the fox, who effaces his tracks in the sand with his tail.— Niels Abel (1802-1829)J.M. Borwein Mahler Measures

Page 89: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Visualizing W4 in the complex plane

• Easily drawn now in Mathematica from recursion andMeijer-G form.

– Each value is coloured differently (black is zero and whiteinfinity). Note the poles and zeros.

J.M. Borwein Mahler Measures

Page 90: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Visualizing W4 in the complex plane

• Easily drawn now in Mathematica from recursion andMeijer-G form.

– Each value is coloured differently (black is zero and whiteinfinity). Note the poles and zeros.

J.M. Borwein Mahler Measures

Page 91: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Visualizing W4 in the complex plane

• Easily drawn now in Mathematica from recursion andMeijer-G form.

– Each value is coloured differently (black is zero and whiteinfinity). Note the poles and zeros.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Simplifying the Meijer integral

Corollary (Hypergeometric forms for non-integer s > −2)

W3(s) =1

22s+1tan

(πs

2

)(ss−12

)2

3F2

(12, 12, 12

s+32, s+3

2

∣∣∣∣ 14

)+

(s

s2

)3F2

(− s

2,− s

2,− s

2

1,− s−12

∣∣∣∣ 14

),

and

W4(s) =1

22stan

(πs

2

)(ss−12

)3

4F3

(12, 12, 12, s2

+ 1

s+32, s+3

2, s+3

2

∣∣∣∣1)

+

(s

s2

)4F3

(12,− s

2,− s

2,− s

2

1, 1,− s−12

∣∣∣∣1).

• We (humans) were able to provably take the limit:

W4(−1) =π

47F6

(54, 12, 12, 12, 12, 12, 12

14, 1, 1, 1, 1, 1

∣∣∣∣1)

4

∞∑n=0

(4n + 1)(2nn

)646n

46F5

(12, 12, 12, 12, 12, 12

1, 1, 1, 1, 1

∣∣∣∣1)

646F5

(32, 32, 32, 32, 32, 32

2, 2, 2, 2, 2

∣∣∣∣1).

• We have proven the corresponding result for W4(1) ....

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Simplifying the Meijer integral

Corollary (Hypergeometric forms for non-integer s > −2)

W3(s) =1

22s+1tan

(πs

2

)(ss−12

)2

3F2

(12, 12, 12

s+32, s+3

2

∣∣∣∣ 14

)+

(s

s2

)3F2

(− s

2,− s

2,− s

2

1,− s−12

∣∣∣∣ 14

),

and

W4(s) =1

22stan

(πs

2

)(ss−12

)3

4F3

(12, 12, 12, s2

+ 1

s+32, s+3

2, s+3

2

∣∣∣∣1)

+

(s

s2

)4F3

(12,− s

2,− s

2,− s

2

1, 1,− s−12

∣∣∣∣1).

• We (humans) were able to provably take the limit:

W4(−1) =π

47F6

(54, 12, 12, 12, 12, 12, 12

14, 1, 1, 1, 1, 1

∣∣∣∣1)

4

∞∑n=0

(4n + 1)(2nn

)646n

46F5

(12, 12, 12, 12, 12, 12

1, 1, 1, 1, 1

∣∣∣∣1)

646F5

(32, 32, 32, 32, 32, 32

2, 2, 2, 2, 2

∣∣∣∣1).

• We have proven the corresponding result for W4(1) ....

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Simplifying the Meijer integral

Corollary (Hypergeometric forms for non-integer s > −2)

W3(s) =1

22s+1tan

(πs

2

)(ss−12

)2

3F2

(12, 12, 12

s+32, s+3

2

∣∣∣∣ 14

)+

(s

s2

)3F2

(− s

2,− s

2,− s

2

1,− s−12

∣∣∣∣ 14

),

and

W4(s) =1

22stan

(πs

2

)(ss−12

)3

4F3

(12, 12, 12, s2

+ 1

s+32, s+3

2, s+3

2

∣∣∣∣1)

+

(s

s2

)4F3

(12,− s

2,− s

2,− s

2

1, 1,− s−12

∣∣∣∣1).

• We (humans) were able to provably take the limit:

W4(−1) =π

47F6

(54, 12, 12, 12, 12, 12, 12

14, 1, 1, 1, 1, 1

∣∣∣∣1)

4

∞∑n=0

(4n + 1)(2nn

)646n

46F5

(12, 12, 12, 12, 12, 12

1, 1, 1, 1, 1

∣∣∣∣1)

646F5

(32, 32, 32, 32, 32, 32

2, 2, 2, 2, 2

∣∣∣∣1).

• We have proven the corresponding result for W4(1) ....

J.M. Borwein Mahler Measures

Page 95: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Simplifying the Meijer integral

Corollary (Hypergeometric forms for non-integer s > −2)

W3(s) =1

22s+1tan

(πs

2

)(ss−12

)2

3F2

(12, 12, 12

s+32, s+3

2

∣∣∣∣ 14

)+

(s

s2

)3F2

(− s

2,− s

2,− s

2

1,− s−12

∣∣∣∣ 14

),

and

W4(s) =1

22stan

(πs

2

)(ss−12

)3

4F3

(12, 12, 12, s2

+ 1

s+32, s+3

2, s+3

2

∣∣∣∣1)

+

(s

s2

)4F3

(12,− s

2,− s

2,− s

2

1, 1,− s−12

∣∣∣∣1).

• We (humans) were able to provably take the limit:

W4(−1) =π

47F6

(54, 12, 12, 12, 12, 12, 12

14, 1, 1, 1, 1, 1

∣∣∣∣1)

4

∞∑n=0

(4n + 1)(2nn

)646n

46F5

(12, 12, 12, 12, 12, 12

1, 1, 1, 1, 1

∣∣∣∣1)

646F5

(32, 32, 32, 32, 32, 32

2, 2, 2, 2, 2

∣∣∣∣1).

• We have proven the corresponding result for W4(1) ....

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Hypergeometric values of W3,W4: from Meijer-G values.

With much work involving moments of elliptic integrals we finallyobtain:

Theorem (Tractable hypergeometric form for W3)

(a) For s 6= −3,−5,−7, . . . , we have

W3(s) =3s+3/2

2πβ

(s+

1

2, s+

1

2

)3F2

(s+2

2 , s+22 , s+2

2

1, s+32

∣∣∣∣14).

(6)

(b) For every natural number k = 1, 2, . . .,

W3(−2k − 1) =

√3(

2kk

)224k+132k 3F2

( 12 ,

12 ,

12

k + 1, k + 1

∣∣∣∣14).

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

A Discovery Demystified: on piecing all this togetherWe first proved that:

W3(2k) =∑

a1+a2+a3=k

(k

a1, a2, a3

)2

= 3F2

(1/2,−k,−k

1, 1

∣∣∣∣4)︸ ︷︷ ︸=:V3(2k)

.

We discovered numerically that: V3(1) = 1.57459− .12602652i

Theorem (Real part)

For all integers k we have W3(k) = Re (V3(k)).

We have a habit in writing articles published in scientific journals to

make the work as finished as possible, to cover up all the tracks, to not

worry about the blind alleys or describe how you had the wrong idea first.

. . . So there isn’t any place to publish, in a dignified manner, what you

actually did in order to get to do the work. — Richard Feynman (Nobel

acceptance 1966)

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

A Discovery Demystified: on piecing all this togetherWe first proved that:

W3(2k) =∑

a1+a2+a3=k

(k

a1, a2, a3

)2

= 3F2

(1/2,−k,−k

1, 1

∣∣∣∣4)︸ ︷︷ ︸=:V3(2k)

.

We discovered numerically that: V3(1) = 1.57459− .12602652i

Theorem (Real part)

For all integers k we have W3(k) = Re (V3(k)).

We have a habit in writing articles published in scientific journals to

make the work as finished as possible, to cover up all the tracks, to not

worry about the blind alleys or describe how you had the wrong idea first.

. . . So there isn’t any place to publish, in a dignified manner, what you

actually did in order to get to do the work. — Richard Feynman (Nobel

acceptance 1966)

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

A Discovery Demystified: on piecing all this togetherWe first proved that:

W3(2k) =∑

a1+a2+a3=k

(k

a1, a2, a3

)2

= 3F2

(1/2,−k,−k

1, 1

∣∣∣∣4)︸ ︷︷ ︸=:V3(2k)

.

We discovered numerically that: V3(1) = 1.57459− .12602652i

Theorem (Real part)

For all integers k we have W3(k) = Re (V3(k)).

We have a habit in writing articles published in scientific journals to

make the work as finished as possible, to cover up all the tracks, to not

worry about the blind alleys or describe how you had the wrong idea first.

. . . So there isn’t any place to publish, in a dignified manner, what you

actually did in order to get to do the work. — Richard Feynman (Nobel

acceptance 1966)

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Closed Forms for W3

• We then confirmed 175 digits of

W3(1) ≈ 1.57459723755189365749 . . .

• Armed with a knowledge of elliptic integrals:

W3(1) =16 3√

4π2

Γ(13)6

+3Γ(1

3)6

8 3√

4π4= W3(−1) +

6/π2

W3(−1),

W3(−1) =3Γ(1

3)6

8 3√

4π4=

213

4π2β2

(1

3

).

Here β(s) := B(s, s) = Γ(s)2

Γ(2s) .

• Obtained via singular values of the elliptic integral andLegendre’s identity.

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Closed Forms for W3

• We then confirmed 175 digits of

W3(1) ≈ 1.57459723755189365749 . . .

• Armed with a knowledge of elliptic integrals:

W3(1) =16 3√

4π2

Γ(13)6

+3Γ(1

3)6

8 3√

4π4= W3(−1) +

6/π2

W3(−1),

W3(−1) =3Γ(1

3)6

8 3√

4π4=

213

4π2β2

(1

3

).

Here β(s) := B(s, s) = Γ(s)2

Γ(2s) .

• Obtained via singular values of the elliptic integral andLegendre’s identity.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Closed Forms for W3

• We then confirmed 175 digits of

W3(1) ≈ 1.57459723755189365749 . . .

• Armed with a knowledge of elliptic integrals:

W3(1) =16 3√

4π2

Γ(13)6

+3Γ(1

3)6

8 3√

4π4= W3(−1) +

6/π2

W3(−1),

W3(−1) =3Γ(1

3)6

8 3√

4π4=

213

4π2β2

(1

3

).

Here β(s) := B(s, s) = Γ(s)2

Γ(2s) .

• Obtained via singular values of the elliptic integral andLegendre’s identity.

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Probability: Bessel function representations

1906. J.C. Kluyver (1860-1932) derived the cumulative radialdistribution function (Pn) and density (pn) of the n-step distance:

Pn(t) = t

∫ ∞0

J1(xt) Jn0 (x) dx

pn(t) = t

∫ ∞0

J0(xt) Jn0 (x)x dx (n ≥ 4) (7)

where Jn(x) is a Bessel function of the first kind• See also Watson (1932, §49) – 3-dim walks are elementary.

• From (9) below, we find

pn(1) = Res−2 (Wn+1) (n 6= 4). (8)

• As p2(α) = 2π√

4−α2, we check in Maple that the following

code returns R = 2/(√

3π) symbolically:R:=identify(evalf[20](int(BesselJ(0,x)^3*x,x=0..infinity)))

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41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

A Bessel Integral for Wn

• Now Pn(1) = J0(0)n+1

n+1 = 1n+1 (Pearson’s original question).

• Broadhurst used (7) for 2k > s > −n2 to write

Wn(s) = 2s+1−k Γ(1 + s2)

Γ(k − s2)

∫ ∞0

x2k−s−1

(−1

x

d

dx

)kJn0 (x)dx,

(9)a useful oscillatory 1-dim integral (used below).

• Thence

Wn(−1) =

∫ ∞0

Jn0 (x)dx, Wn(1) = n

∫ ∞0

J1(x)J0(x)n−1 dx

x.

(10)

Integrands for W4(−1) (blue) andW4(1) (red) on [π, 4π] from (10).

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

A Bessel Integral for Wn

• Now Pn(1) = J0(0)n+1

n+1 = 1n+1 (Pearson’s original question).

• Broadhurst used (7) for 2k > s > −n2 to write

Wn(s) = 2s+1−k Γ(1 + s2)

Γ(k − s2)

∫ ∞0

x2k−s−1

(−1

x

d

dx

)kJn0 (x)dx,

(9)a useful oscillatory 1-dim integral (used below).

• Thence

Wn(−1) =

∫ ∞0

Jn0 (x)dx, Wn(1) = n

∫ ∞0

J1(x)J0(x)n−1 dx

x.

(10)

Integrands for W4(−1) (blue) andW4(1) (red) on [π, 4π] from (10).

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

A Bessel Integral for Wn

• Now Pn(1) = J0(0)n+1

n+1 = 1n+1 (Pearson’s original question).

• Broadhurst used (7) for 2k > s > −n2 to write

Wn(s) = 2s+1−k Γ(1 + s2)

Γ(k − s2)

∫ ∞0

x2k−s−1

(−1

x

d

dx

)kJn0 (x)dx,

(9)a useful oscillatory 1-dim integral (used below).

• Thence

Wn(−1) =

∫ ∞0

Jn0 (x)dx, Wn(1) = n

∫ ∞0

J1(x)J0(x)n−1 dx

x.

(10)

Integrands for W4(−1) (blue) andW4(1) (red) on [π, 4π] from (10).

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

A Bessel Integral for Wn

• Now Pn(1) = J0(0)n+1

n+1 = 1n+1 (Pearson’s original question).

• Broadhurst used (7) for 2k > s > −n2 to write

Wn(s) = 2s+1−k Γ(1 + s2)

Γ(k − s2)

∫ ∞0

x2k−s−1

(−1

x

d

dx

)kJn0 (x)dx,

(9)a useful oscillatory 1-dim integral (used below).

• Thence

Wn(−1) =

∫ ∞0

Jn0 (x)dx, Wn(1) = n

∫ ∞0

J1(x)J0(x)n−1 dx

x.

(10)

Integrands for W4(−1) (blue) andW4(1) (red) on [π, 4π] from (10).

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

The Densities for n = 3, 4 are ModularLet σ(x) := 3−x

1+x . Then σ is an involution on [0, 3] sending [0, 1] to [1, 3]:

p3(x) =4x

(3− x)(x+ 1)p3(σ(x)).

So 34p′3(0) = p3(3) =

√3

2π , p(1) =∞. We found:

p3(α) =2√3α

π(3 + α2

) 2F1

13, 23

1

∣∣∣∣α2(9− α2

)2(3 + α2

)3 =

2√3

π

α

AG3(3 + α2, 3(1− α2

)2/3)where AG3 is the cubically convergent mean iteration (1991):

AG3(a, b) :=a + 2b

3

⊗(b ·

a2 + ab + b2

3

)1/3

. The densities p3 (L) and p4 (R)

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

The Densities for n = 3, 4 are ModularLet σ(x) := 3−x

1+x . Then σ is an involution on [0, 3] sending [0, 1] to [1, 3]:

p3(x) =4x

(3− x)(x+ 1)p3(σ(x)).

So 34p′3(0) = p3(3) =

√3

2π , p(1) =∞. We found:

p3(α) =2√3α

π(3 + α2

) 2F1

13, 23

1

∣∣∣∣α2(9− α2

)2(3 + α2

)3 =

2√3

π

α

AG3(3 + α2, 3(1− α2

)2/3)where AG3 is the cubically convergent mean iteration (1991):

AG3(a, b) :=a + 2b

3

⊗(b ·

a2 + ab + b2

3

)1/3

. The densities p3 (L) and p4 (R)

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Formula for the ‘shark-fin’ p4

We ultimately deduce on 2 ≤ α ≤ 4 a hyper-closed form:

p4(α) =2

π2

√16− α2

α3F2

(12 ,

12 ,

12

56 ,

76

∣∣∣∣(16− α2

)3108α4

). (11)

← p4 from (11) vs 18-terms of series

X Proves p4(2) = 27/3π3√3

Γ(23

)−6=

√3π W3(−1) ≈ 0.494233 < 1

2

• Marvelously, we found — and provedby a subtle use of distributional Mellintransforms — that on [0, 2] as well:

p4(α) =2

π2

√16− α2

αRe 3F2

(12 ,

12 ,

12

56 ,

76

∣∣∣∣(16− α2

)3108α4

)(Discovering this Re brought us full circle.)

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Formula for the ‘shark-fin’ p4

We ultimately deduce on 2 ≤ α ≤ 4 a hyper-closed form:

p4(α) =2

π2

√16− α2

α3F2

(12 ,

12 ,

12

56 ,

76

∣∣∣∣(16− α2

)3108α4

). (11)

← p4 from (11) vs 18-terms of series

X Proves p4(2) = 27/3π3√3

Γ(23

)−6=

√3π W3(−1) ≈ 0.494233 < 1

2

• Marvelously, we found — and provedby a subtle use of distributional Mellintransforms — that on [0, 2] as well:

p4(α) =2

π2

√16− α2

αRe 3F2

(12 ,

12 ,

12

56 ,

76

∣∣∣∣(16− α2

)3108α4

)(Discovering this Re brought us full circle.)

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Densities for 5 ≤ n ≤ 8 (and large n approximation)

1 2 3 4 5

0.05

0.10

0.15

0.20

0.25

0.30

0.35

1 2 3 4 5 6

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Both p2n+4, p2n+5 are n-times continuously differentiable for x > 0

(pn(x) ∼ 2xn e−x2/n). So “four is small” but “eight is large.”

1 2 3 4 5 6 7

0.05

0.10

0.15

0.20

0.25

0.30

2 4 6 8

0.05

0.10

0.15

0.20

0.25

0.30

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Densities for 5 ≤ n ≤ 8 (and large n approximation)

1 2 3 4 5

0.05

0.10

0.15

0.20

0.25

0.30

0.35

1 2 3 4 5 6

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Both p2n+4, p2n+5 are n-times continuously differentiable for x > 0

(pn(x) ∼ 2xn e−x2/n). So “four is small” but “eight is large.”

1 2 3 4 5 6 7

0.05

0.10

0.15

0.20

0.25

0.30

2 4 6 8

0.05

0.10

0.15

0.20

0.25

0.30

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

The Five Step Walk

• The functional equation for W5 is:

225(s + 4)2(s + 2)

2W5(s) = −(35(s + 5)

4+ 42(s + 5)

2+ 3)W5(s + 4)

+ (s + 6)4W5(s + 6) + (s + 4)

2(259(s + 4)

2+ 104)W5(s + 2).

• We deduce the first two poles — and so all — are simple since

lims→−2

(s+ 2)2W5(s) =4

225(285W5(0)− 201W5(2) + 16W5(4)) = 0

lims→−4

(s+ 4)2W5(s) = − 4

225(5W5(0)−W5(2)) = 0.

• We stumbled upon

p4(1) =Res−2(W5) =

√15

3π3F2

( 13 ,

23 ,

12

1, 1

∣∣∣∣−4

).

??? Is there a hyper-closed form for W5(∓1) ???J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

The Five Step Walk

• The functional equation for W5 is:

225(s + 4)2(s + 2)

2W5(s) = −(35(s + 5)

4+ 42(s + 5)

2+ 3)W5(s + 4)

+ (s + 6)4W5(s + 6) + (s + 4)

2(259(s + 4)

2+ 104)W5(s + 2).

• We deduce the first two poles — and so all — are simple since

lims→−2

(s+ 2)2W5(s) =4

225(285W5(0)− 201W5(2) + 16W5(4)) = 0

lims→−4

(s+ 4)2W5(s) = − 4

225(5W5(0)−W5(2)) = 0.

• We stumbled upon

p4(1) =Res−2(W5) =

√15

3π3F2

( 13 ,

23 ,

12

1, 1

∣∣∣∣−4

).

??? Is there a hyper-closed form for W5(∓1) ???J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

The Five Step Walk

• The functional equation for W5 is:

225(s + 4)2(s + 2)

2W5(s) = −(35(s + 5)

4+ 42(s + 5)

2+ 3)W5(s + 4)

+ (s + 6)4W5(s + 6) + (s + 4)

2(259(s + 4)

2+ 104)W5(s + 2).

• We deduce the first two poles — and so all — are simple since

lims→−2

(s+ 2)2W5(s) =4

225(285W5(0)− 201W5(2) + 16W5(4)) = 0

lims→−4

(s+ 4)2W5(s) = − 4

225(5W5(0)−W5(2)) = 0.

• We stumbled upon

p4(1) =Res−2(W5) =

√15

3π3F2

( 13 ,

23 ,

12

1, 1

∣∣∣∣−4

).

??? Is there a hyper-closed form for W5(∓1) ???J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

The Five Step Walk

• The functional equation for W5 is:

225(s + 4)2(s + 2)

2W5(s) = −(35(s + 5)

4+ 42(s + 5)

2+ 3)W5(s + 4)

+ (s + 6)4W5(s + 6) + (s + 4)

2(259(s + 4)

2+ 104)W5(s + 2).

• We deduce the first two poles — and so all — are simple since

lims→−2

(s+ 2)2W5(s) =4

225(285W5(0)− 201W5(2) + 16W5(4)) = 0

lims→−4

(s+ 4)2W5(s) = − 4

225(5W5(0)−W5(2)) = 0.

• We stumbled upon

p4(1) =Res−2(W5) =

√15

3π3F2

( 13 ,

23 ,

12

1, 1

∣∣∣∣−4

).

??? Is there a hyper-closed form for W5(∓1) ???J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

W5 and p5: Bessel integrals are hard

• We only know Res−4(W5) numerically — but to 500 digits:• by Bailey in about 5.5hrs on 1 MacPro core.

– Sidi-“mW” method used: i.e., Gaussian quadrature onintervals of [nπ, (n+ 1)π] plus Richardson-like extrapolation.

– Can r5(2) be identified?

• Here r5(k) := Res(−2k)(W5). Other residues are thencombinations as follows:

• From the W5-recursion: given r5(0) = 0, r5(1) and r5(2) we have

r5(k + 3) =k4r5(k)−

(5 + 28 k + 63 k2 + 70 k3 + 35 k4

)r5(k + 1)

225(k + 1)2(k + 2)2

+

(285 + 518 k + 259 k2

)r5(k + 2)

225(k + 2)2.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

W5 and p5: Bessel integrals are hard

• We only know Res−4(W5) numerically — but to 500 digits:• by Bailey in about 5.5hrs on 1 MacPro core.

– Sidi-“mW” method used: i.e., Gaussian quadrature onintervals of [nπ, (n+ 1)π] plus Richardson-like extrapolation.

– Can r5(2) be identified?

• Here r5(k) := Res(−2k)(W5). Other residues are thencombinations as follows:

• From the W5-recursion: given r5(0) = 0, r5(1) and r5(2) we have

r5(k + 3) =k4r5(k)−

(5 + 28 k + 63 k2 + 70 k3 + 35 k4

)r5(k + 1)

225(k + 1)2(k + 2)2

+

(285 + 518 k + 259 k2

)r5(k + 2)

225(k + 2)2.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

W5 and p5: Bessel integrals are hard

• We only know Res−4(W5) numerically — but to 500 digits:• by Bailey in about 5.5hrs on 1 MacPro core.

– Sidi-“mW” method used: i.e., Gaussian quadrature onintervals of [nπ, (n+ 1)π] plus Richardson-like extrapolation.

– Can r5(2) be identified?

• Here r5(k) := Res(−2k)(W5). Other residues are thencombinations as follows:

• From the W5-recursion: given r5(0) = 0, r5(1) and r5(2) we have

r5(k + 3) =k4r5(k)−

(5 + 28 k + 63 k2 + 70 k3 + 35 k4

)r5(k + 1)

225(k + 1)2(k + 2)2

+

(285 + 518 k + 259 k2

)r5(k + 2)

225(k + 2)2.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

W5 and p5: Bessel integrals are hard

• We only know Res−4(W5) numerically — but to 500 digits:• by Bailey in about 5.5hrs on 1 MacPro core.

– Sidi-“mW” method used: i.e., Gaussian quadrature onintervals of [nπ, (n+ 1)π] plus Richardson-like extrapolation.

– Can r5(2) be identified?

• Here r5(k) := Res(−2k)(W5). Other residues are thencombinations as follows:

• From the W5-recursion: given r5(0) = 0, r5(1) and r5(2) we have

r5(k + 3) =k4r5(k)−

(5 + 28 k + 63 k2 + 70 k3 + 35 k4

)r5(k + 1)

225(k + 1)2(k + 2)2

+

(285 + 518 k + 259 k2

)r5(k + 2)

225(k + 2)2.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

W5 and p5: Bessel integrals are hard

• We only know Res−4(W5) numerically — but to 500 digits:• by Bailey in about 5.5hrs on 1 MacPro core.

– Sidi-“mW” method used: i.e., Gaussian quadrature onintervals of [nπ, (n+ 1)π] plus Richardson-like extrapolation.

– Can r5(2) be identified?

• Here r5(k) := Res(−2k)(W5). Other residues are thencombinations as follows:

• From the W5-recursion: given r5(0) = 0, r5(1) and r5(2) we have

r5(k + 3) =k4r5(k)−

(5 + 28 k + 63 k2 + 70 k3 + 35 k4

)r5(k + 1)

225(k + 1)2(k + 2)2

+

(285 + 518 k + 259 k2

)r5(k + 2)

225(k + 2)2.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

W5 and p5: Bessel integrals can be hard

0.5 1.0 1.5 2.0 2.5

0.2

0.4

0.6

0.8

1 2 3 4 5

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Figure: The series at zero and p5.• 1963. Fettis first rigorously established nonlinearity. A few more

residues yield p5(x) = 0.329934x+ 0.00661673x3 +0.000262333x5 + 0.0000141185x7 +O(x9)

Hence the strikingly straight shape of p5(x) on [0, 1] :

“the graphical construction, however carefully reinvestigated, did not per-mit of our considering the curve to be anything but a straight line. . . Evenif it is not absolutely true, it exemplifies the extraordinary power of suchintegrals of J products to give extremely close approximations to suchsimple forms as horizontal lines.” — Karl Pearson (1906)

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

W5 and p5: Bessel integrals can be hard

0.5 1.0 1.5 2.0 2.5

0.2

0.4

0.6

0.8

1 2 3 4 5

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Figure: The series at zero and p5.• 1963. Fettis first rigorously established nonlinearity. A few more

residues yield p5(x) = 0.329934x+ 0.00661673x3 +0.000262333x5 + 0.0000141185x7 +O(x9)

Hence the strikingly straight shape of p5(x) on [0, 1] :

“the graphical construction, however carefully reinvestigated, did not per-mit of our considering the curve to be anything but a straight line. . . Evenif it is not absolutely true, it exemplifies the extraordinary power of suchintegrals of J products to give extremely close approximations to suchsimple forms as horizontal lines.” — Karl Pearson (1906)

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

W5 and p5: Bessel integrals can be hard

0.5 1.0 1.5 2.0 2.5

0.2

0.4

0.6

0.8

1 2 3 4 5

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Figure: The series at zero and p5.• 1963. Fettis first rigorously established nonlinearity. A few more

residues yield p5(x) = 0.329934x+ 0.00661673x3 +0.000262333x5 + 0.0000141185x7 +O(x9)

Hence the strikingly straight shape of p5(x) on [0, 1] :

“the graphical construction, however carefully reinvestigated, did not per-mit of our considering the curve to be anything but a straight line. . . Evenif it is not absolutely true, it exemplifies the extraordinary power of suchintegrals of J products to give extremely close approximations to suchsimple forms as horizontal lines.” — Karl Pearson (1906)

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Short Random Walks: Derivatives of W3,W4

From the hypergeometric forms above we get:

W ′3(0) =1

π3F2

(12 ,

12 ,

12

32 ,

32

∣∣∣∣14)

=1

πCl(π

3

). (12)

The last equality follows from setting θ = π/6 in the identity

2 sin(θ)3F2

(12 ,

12 ,

12

32 ,

32

∣∣∣∣sin2 θ

)= Cl (2 θ) + 2 θ log (2 sin θ) .

Also

W ′4(0) =4

π2 4F3

(12 ,

12 ,

12 , 1

32 ,

32 ,

32

∣∣∣∣1)

=7ζ(3)

2π2. (13)

Here Cl(θ) :=∑∞

n=1sin(nθ)

n2 is Clausen’s function. Likewise:

W′3(2) =

3

πCl(π

3

)− 3√

3

2π+ 2 . . .

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Short Random Walks: Derivatives of W3,W4

From the hypergeometric forms above we get:

W ′3(0) =1

π3F2

(12 ,

12 ,

12

32 ,

32

∣∣∣∣14)

=1

πCl(π

3

). (12)

The last equality follows from setting θ = π/6 in the identity

2 sin(θ)3F2

(12 ,

12 ,

12

32 ,

32

∣∣∣∣sin2 θ

)= Cl (2 θ) + 2 θ log (2 sin θ) .

Also

W ′4(0) =4

π2 4F3

(12 ,

12 ,

12 , 1

32 ,

32 ,

32

∣∣∣∣1)

=7ζ(3)

2π2. (13)

Here Cl(θ) :=∑∞

n=1sin(nθ)

n2 is Clausen’s function. Likewise:

W′3(2) =

3

πCl(π

3

)− 3√

3

2π+ 2 . . .

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

19. Combinatorics25. Meijer-G functions30. Hypergeometric values of W3,W433. Probability and Bessel J41. Derivative values of W3,W4

Short Random Walks: Derivatives of W3,W4

From the hypergeometric forms above we get:

W ′3(0) =1

π3F2

(12 ,

12 ,

12

32 ,

32

∣∣∣∣14)

=1

πCl(π

3

). (12)

The last equality follows from setting θ = π/6 in the identity

2 sin(θ)3F2

(12 ,

12 ,

12

32 ,

32

∣∣∣∣sin2 θ

)= Cl (2 θ) + 2 θ log (2 sin θ) .

Also

W ′4(0) =4

π2 4F3

(12 ,

12 ,

12 , 1

32 ,

32 ,

32

∣∣∣∣1)

=7ζ(3)

2π2. (13)

Here Cl(θ) :=∑∞

n=1sin(nθ)

n2 is Clausen’s function. Likewise:

W′3(2) =

3

πCl(π

3

)− 3√

3

2π+ 2 . . .

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

43. Relations to η44. Smyth’s results revisited46. Boyd’s Conjectures48. A Bonus Measure

Multiple Mahler Measures: for P1, P2, . . . , Pm toc

µ(P1, P2, . . . , Pm) :=

∫ 1

0

· · ·∫ 1

0

m∏k=1

log∣∣Pk (e2πiθ1 , · · · , e2πiθn)∣∣ dθ1 · · · dθn,

was introduced by Sasaki (2010); while

µm(P ) := µ(P,P, . . . , P ), (µ1(P ) = µ(P ))

is a higher Mahler measure as in Kurokawa, Lalın and Ochiai(2008). Also

µm

(1 +

n−1∑k=1

xk

)= W (m)

n (0), (14)

was evaluated in (12), (13) for n = 3 and n = 4 and m = 1:

1 µ(1 + x+ y) = L′3(−1) = 1

π Cl(π3

)(Smyth)

2 µ(1 + x+ y + z) = 14 ζ′(−2) = 7

2ζ(3)π2 (Smyth)

– So (14) recaptured both Smyth’s results.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

43. Relations to η44. Smyth’s results revisited46. Boyd’s Conjectures48. A Bonus Measure

Multiple Mahler Measures: for P1, P2, . . . , Pm toc

µ(P1, P2, . . . , Pm) :=

∫ 1

0

· · ·∫ 1

0

m∏k=1

log∣∣Pk (e2πiθ1 , · · · , e2πiθn)∣∣ dθ1 · · · dθn,

was introduced by Sasaki (2010); while

µm(P ) := µ(P,P, . . . , P ), (µ1(P ) = µ(P ))

is a higher Mahler measure as in Kurokawa, Lalın and Ochiai(2008). Also

µm

(1 +

n−1∑k=1

xk

)= W (m)

n (0), (14)

was evaluated in (12), (13) for n = 3 and n = 4 and m = 1:

1 µ(1 + x+ y) = L′3(−1) = 1

π Cl(π3

)(Smyth)

2 µ(1 + x+ y + z) = 14 ζ′(−2) = 7

2ζ(3)π2 (Smyth)

– So (14) recaptured both Smyth’s results.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

43. Relations to η44. Smyth’s results revisited46. Boyd’s Conjectures48. A Bonus Measure

Multiple Mahler Measures: for P1, P2, . . . , Pm toc

µ(P1, P2, . . . , Pm) :=

∫ 1

0

· · ·∫ 1

0

m∏k=1

log∣∣Pk (e2πiθ1 , · · · , e2πiθn)∣∣ dθ1 · · · dθn,

was introduced by Sasaki (2010); while

µm(P ) := µ(P,P, . . . , P ), (µ1(P ) = µ(P ))

is a higher Mahler measure as in Kurokawa, Lalın and Ochiai(2008). Also

µm

(1 +

n−1∑k=1

xk

)= W (m)

n (0), (14)

was evaluated in (12), (13) for n = 3 and n = 4 and m = 1:

1 µ(1 + x+ y) = L′3(−1) = 1

π Cl(π3

)(Smyth)

2 µ(1 + x+ y + z) = 14 ζ′(−2) = 7

2ζ(3)π2 (Smyth)

– So (14) recaptured both Smyth’s results.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

43. Relations to η44. Smyth’s results revisited46. Boyd’s Conjectures48. A Bonus Measure

Multiple Mahler Measures: for P1, P2, . . . , Pm toc

µ(P1, P2, . . . , Pm) :=

∫ 1

0

· · ·∫ 1

0

m∏k=1

log∣∣Pk (e2πiθ1 , · · · , e2πiθn)∣∣ dθ1 · · · dθn,

was introduced by Sasaki (2010); while

µm(P ) := µ(P,P, . . . , P ), (µ1(P ) = µ(P ))

is a higher Mahler measure as in Kurokawa, Lalın and Ochiai(2008). Also

µm

(1 +

n−1∑k=1

xk

)= W (m)

n (0), (14)

was evaluated in (12), (13) for n = 3 and n = 4 and m = 1:

1 µ(1 + x+ y) = L′3(−1) = 1

π Cl(π3

)(Smyth)

2 µ(1 + x+ y + z) = 14 ζ′(−2) = 7

2ζ(3)π2 (Smyth)

– So (14) recaptured both Smyth’s results.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

43. Relations to η44. Smyth’s results revisited46. Boyd’s Conjectures48. A Bonus Measure

Multiple Mahler Measures: for P1, P2, . . . , Pm toc

µ(P1, P2, . . . , Pm) :=

∫ 1

0

· · ·∫ 1

0

m∏k=1

log∣∣Pk (e2πiθ1 , · · · , e2πiθn)∣∣ dθ1 · · · dθn,

was introduced by Sasaki (2010); while

µm(P ) := µ(P,P, . . . , P ), (µ1(P ) = µ(P ))

is a higher Mahler measure as in Kurokawa, Lalın and Ochiai(2008). Also

µm

(1 +

n−1∑k=1

xk

)= W (m)

n (0), (14)

was evaluated in (12), (13) for n = 3 and n = 4 and m = 1:

1 µ(1 + x+ y) = L′3(−1) = 1

π Cl(π3

)(Smyth)

2 µ(1 + x+ y + z) = 14 ζ′(−2) = 7

2ζ(3)π2 (Smyth)

– So (14) recaptured both Smyth’s results.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

43. Relations to η44. Smyth’s results revisited46. Boyd’s Conjectures48. A Bonus Measure

Relations to Dedekind’s η

Denninger’s 1997 conjecture, proven recently by Rogers andZudilin (2011), is

µ(1 + x+ y + 1/x+ 1/y)?=

15

4π2LE(2)

– an L-series value for an elliptic curve E with conductor 15.

• For (14) with n = 5, 6 conjectures of Villegas become:

W′

5(0)?=

(15

4π2

)5/2 ∫ ∞0

{η3(e−3t)η3(e−5t) + η3(e−t)η3(e−15t)

}t3 dt

W′

6(0)?=

(3

π2

)3 ∫ ∞0

η2(e−t)η2(e−2t)η2(e−3t)η2(e−6t) t4 dt

where Dedekind’s η is η(q) := q1/24∑∞

n=−∞(−1)nqn(3n+1)/4.

• Confirmed to 600 and to 80 digits respectively.

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

43. Relations to η44. Smyth’s results revisited46. Boyd’s Conjectures48. A Bonus Measure

Relations to Dedekind’s η

Denninger’s 1997 conjecture, proven recently by Rogers andZudilin (2011), is

µ(1 + x+ y + 1/x+ 1/y)?=

15

4π2LE(2)

– an L-series value for an elliptic curve E with conductor 15.

• For (14) with n = 5, 6 conjectures of Villegas become:

W′

5(0)?=

(15

4π2

)5/2 ∫ ∞0

{η3(e−3t)η3(e−5t) + η3(e−t)η3(e−15t)

}t3 dt

W′

6(0)?=

(3

π2

)3 ∫ ∞0

η2(e−t)η2(e−2t)η2(e−3t)η2(e−6t) t4 dt

where Dedekind’s η is η(q) := q1/24∑∞

n=−∞(−1)nqn(3n+1)/4.

• Confirmed to 600 and to 80 digits respectively.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

43. Relations to η44. Smyth’s results revisited46. Boyd’s Conjectures48. A Bonus Measure

Relations to Dedekind’s η

Denninger’s 1997 conjecture, proven recently by Rogers andZudilin (2011), is

µ(1 + x+ y + 1/x+ 1/y)?=

15

4π2LE(2)

– an L-series value for an elliptic curve E with conductor 15.

• For (14) with n = 5, 6 conjectures of Villegas become:

W′

5(0)?=

(15

4π2

)5/2 ∫ ∞0

{η3(e−3t)η3(e−5t) + η3(e−t)η3(e−15t)

}t3 dt

W′

6(0)?=

(3

π2

)3 ∫ ∞0

η2(e−t)η2(e−2t)η2(e−3t)η2(e−6t) t4 dt

where Dedekind’s η is η(q) := q1/24∑∞

n=−∞(−1)nqn(3n+1)/4.

• Confirmed to 600 and to 80 digits respectively.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

43. Relations to η44. Smyth’s results revisited46. Boyd’s Conjectures48. A Bonus Measure

µ(1 + x+ y) and µ(1 + x+ y + z) revisited

We recall:

Lemma (Jensen’s formula)∫ 1

0log∣∣α+ e2πi t

∣∣ dt = log (max{|α|, 1}) . (15)

We use (15) to reduce to a one dimensional integral:

µ(1 + x+ y) =

∫ 5/6

1/6log(2 sin(πy)) dy =

1

πLs2

(π3

)=

1

πCl2

(π3

),

which is (12).

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

43. Relations to η44. Smyth’s results revisited46. Boyd’s Conjectures48. A Bonus Measure

µ(1 + x+ y) and µ(1 + x+ y + z) revisited

Following Boyd, on applying Jensen’s formula, for complex a and bwe have µ(ax+ b) = log |a| ∨ log |b|. Let w := y/z. We now write

µ(1 + x+ y + z) = µ(1 + x+ z(1 + w)) = µ(log |1 + w| ∨ log |1 + x|)

=1

π2

∫ π

0

∫ π

0

max

{log

(2 sin

θ

2

), log 2

(sin

t

2

)}dt

=2

π2

∫ π

0

∫ θ

0

log

(2 sin

θ

2

)dt

=2

π2

∫ π

0

θ log

(2 sin

θ

2

)dθ

= − 2

π2Ls

(1)3 (π) =

7

2

ζ(3)

π2,

which is (13).

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

43. Relations to η44. Smyth’s results revisited46. Boyd’s Conjectures48. A Bonus Measure

Boyd’s 1998 Conjectures

Theorem (Two quadratic evaluations)

Below L−n is a primitive L-series and G is Catalan’s constant.

µ3 := µ(y2(x+ 1)2 + y(x2 + 6x+ 1) + (x+ 1)2) =16

3πL−4(2)

=16

3πG,

µ−5 := µ(y2(x+ 1)2 + y(x2 − 10x+ 1) + (x+ 1)2) =5√

3

πL−3(2)

=20

3πCl2

(π3

).

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

43. Relations to η44. Smyth’s results revisited46. Boyd’s Conjectures48. A Bonus Measure

Log-sine Integrals are Again Inside

First proven in 2008 using Bloch-Wigner logarithms, we used avariant of Jensen’s formula and slick trigonometry to arrive at:

µ3 =1

π

∫ π

0log(1 + 4| cos θ|+ 4| cos2 θ|) dθ

=4

π

∫ π/2

0log(1 + 2 cos θ) dθ

=4

π

∫ π/2

0log

(2 sin 3θ

2

2 sin θ2

)dθ

=4

(Ls2

(3π

2

)− 3 Ls2

(π2

))=

16

3

L−4(2)

π

as needed, since Ls2

(3π2

)= −Ls2

(π2

)= L−4(2), which is

Catalan’s G.J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

43. Relations to η44. Smyth’s results revisited46. Boyd’s Conjectures48. A Bonus Measure

Log-sine Integrals are Again Inside

First proven in 2008 using Bloch-Wigner logarithms, we used avariant of Jensen’s formula and slick trigonometry to arrive at:

µ3 =1

π

∫ π

0log(1 + 4| cos θ|+ 4| cos2 θ|) dθ

=4

π

∫ π/2

0log(1 + 2 cos θ) dθ

=4

π

∫ π/2

0log

(2 sin 3θ

2

2 sin θ2

)dθ

=4

(Ls2

(3π

2

)− 3 Ls2

(π2

))=

16

3

L−4(2)

π

as needed, since Ls2

(3π2

)= −Ls2

(π2

)= L−4(2), which is

Catalan’s G.J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

43. Relations to η44. Smyth’s results revisited46. Boyd’s Conjectures48. A Bonus Measure

And More

Much the same techniques work for µ5 and there is one bonus case:

Theorem

π µ−1 = π µ((x+ 1)2(y2 + y + 1)− 2xy

)=

1

2B

(1

4,

1

4

)3F2

(14 ,

14 , 1

34 ,

54

∣∣∣∣14)− 1

6B

(3

4,

3

4

)3F2

(34 ,

34 , 1

54 ,

74

∣∣∣∣14)

An alternative form of µ−1 is given by

µ−1 = µ((x+ 1/x+ 2

√1/x)

(y + 1/y + 1)− 2).

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

43. Relations to η44. Smyth’s results revisited46. Boyd’s Conjectures48. A Bonus Measure

And More

Much the same techniques work for µ5 and there is one bonus case:

Theorem

π µ−1 = π µ((x+ 1)2(y2 + y + 1)− 2xy

)=

1

2B

(1

4,

1

4

)3F2

(14 ,

14 , 1

34 ,

54

∣∣∣∣14)− 1

6B

(3

4,

3

4

)3F2

(34 ,

34 , 1

54 ,

74

∣∣∣∣14)

An alternative form of µ−1 is given by

µ−1 = µ((x+ 1/x+ 2

√1/x)

(y + 1/y + 1)− 2).

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Sasaki’s Multiple Mahler Measures toc

µk(1 + x+ y∗) := µ(1 + x+ y1, 1 + x+ y2, . . . , 1 + x+ yk)

was studied by Sasaki (2010). He used (15) to observe that

µk(1 + x+ y∗) = −∫ 5/6

1/6logk

∣∣1 + e2πi t∣∣ dt (16)

and so provides an evaluation of µ2(1 + x+ y∗). Immediately from(16) and the definition of the log-sine integrals we have:

Theorem (For k = 1, 2, ...)

µk(1 + x+ y∗) =1

π

{Lsk+1

(π3

)− Lsk+1 (π)

}, (17)

where Lsk+1 is as given by (1).

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Sasaki’s Multiple Mahler Measures toc

µk(1 + x+ y∗) := µ(1 + x+ y1, 1 + x+ y2, . . . , 1 + x+ yk)

was studied by Sasaki (2010). He used (15) to observe that

µk(1 + x+ y∗) = −∫ 5/6

1/6logk

∣∣1 + e2πi t∣∣ dt (16)

and so provides an evaluation of µ2(1 + x+ y∗). Immediately from(16) and the definition of the log-sine integrals we have:

Theorem (For k = 1, 2, ...)

µk(1 + x+ y∗) =1

π

{Lsk+1

(π3

)− Lsk+1 (π)

}, (17)

where Lsk+1 is as given by (1).

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Lsk (π) and Ls(k)n (π)

− 1

π

∞∑m=0

Lsm+1 (π)um

m!=

Γ (1 + u)

Γ2(1 + u

2

) =

(u

u/2

). (18)

Example (Values of Lsn (π))

For instance, we have Ls2 (π) = 0 as well as

−Ls3 (π) =1

12π3 Ls4 (π) =

3

2π ζ(3)

−Ls5 (π) =19

240π5 Ls6 (π) =

45

2π ζ(5) +

5

4π3ζ(3)

−Ls7 (π) =275

1344π7 +

45

2π ζ2(3)

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Lsn (π) and Ls(k)n (π)

Equation (18) is made for a CAS (Mma, Sage or Maple):for k to 7 do

simplify(subs(x=0,diff(Pi*binomial(x,x/2),x$k))) od

We studied general log-sine evaluations with an emphasis onautomatic provable evaluations. For example:

Theorem (Borwein-Straub)

For 2|µ| < λ < 1 we have

−∑n,k≥0

Ls(k)n+k+1 (π)

λn

n!

(iµ)k

k!= i∑n≥0

n

)(−1)neiπ

λ2 − eiπµ

µ− λ2 + n

.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Lsn (π) and Ls(k)n (π)

Equation (18) is made for a CAS (Mma, Sage or Maple):for k to 7 do

simplify(subs(x=0,diff(Pi*binomial(x,x/2),x$k))) od

We studied general log-sine evaluations with an emphasis onautomatic provable evaluations. For example:

Theorem (Borwein-Straub)

For 2|µ| < λ < 1 we have

−∑n,k≥0

Ls(k)n+k+1 (π)

λn

n!

(iµ)k

k!= i∑n≥0

n

)(−1)neiπ

λ2 − eiπµ

µ− λ2 + n

.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Log-sine-cosine integrals toc

The log-sine-cosine integrals

Lscm,n (σ) := −∫ σ

0logm−1

∣∣∣∣2 sinθ

2

∣∣∣∣ logn−1

∣∣∣∣2 cosθ

2

∣∣∣∣ dθ (19)

appear in QFT/physical applications as well. Lewin sketches howvalues at σ = π may be obtained much as for log-sine integrals.• Lewin’s ideas lead to:

− 1

π

∞∑m,n=0

Lscm+1,n+1 (π)xm

m!yn

n! = 2x+y

π

Γ( 1+x2 )Γ( 1+y

2 )Γ(1+x+y

2 )

=(xx/2

)( yy/2

)Γ(1+x2 )Γ(1+ y

2 )Γ(1+x+y

2 ).

• The last form makes it clear that this is an extension of (18).J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Log-sine-cosine integrals toc

The log-sine-cosine integrals

Lscm,n (σ) := −∫ σ

0logm−1

∣∣∣∣2 sinθ

2

∣∣∣∣ logn−1

∣∣∣∣2 cosθ

2

∣∣∣∣ dθ (19)

appear in QFT/physical applications as well. Lewin sketches howvalues at σ = π may be obtained much as for log-sine integrals.• Lewin’s ideas lead to:

− 1

π

∞∑m,n=0

Lscm+1,n+1 (π)xm

m!yn

n! = 2x+y

π

Γ( 1+x2 )Γ( 1+y

2 )Γ(1+x+y

2 )

=(xx/2

)( yy/2

)Γ(1+x2 )Γ(1+ y

2 )Γ(1+x+y

2 ).

• The last form makes it clear that this is an extension of (18).J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Log-sine-cosine integrals toc

The log-sine-cosine integrals

Lscm,n (σ) := −∫ σ

0logm−1

∣∣∣∣2 sinθ

2

∣∣∣∣ logn−1

∣∣∣∣2 cosθ

2

∣∣∣∣ dθ (19)

appear in QFT/physical applications as well. Lewin sketches howvalues at σ = π may be obtained much as for log-sine integrals.• Lewin’s ideas lead to:

− 1

π

∞∑m,n=0

Lscm+1,n+1 (π)xm

m!yn

n! = 2x+y

π

Γ( 1+x2 )Γ( 1+y

2 )Γ(1+x+y

2 )

=(xx/2

)( yy/2

)Γ(1+x2 )Γ(1+ y

2 )Γ(1+x+y

2 ).

• The last form makes it clear that this is an extension of (18).J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Log-sine-cosine integrals toc

The log-sine-cosine integrals

Lscm,n (σ) := −∫ σ

0logm−1

∣∣∣∣2 sinθ

2

∣∣∣∣ logn−1

∣∣∣∣2 cosθ

2

∣∣∣∣ dθ (19)

appear in QFT/physical applications as well. Lewin sketches howvalues at σ = π may be obtained much as for log-sine integrals.• Lewin’s ideas lead to:

− 1

π

∞∑m,n=0

Lscm+1,n+1 (π)xm

m!yn

n! = 2x+y

π

Γ( 1+x2 )Γ( 1+y

2 )Γ(1+x+y

2 )

=(xx/2

)( yy/2

)Γ(1+x2 )Γ(1+ y

2 )Γ(1+x+y

2 ).

• The last form makes it clear that this is an extension of (18).J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Ls(k)n (τ) is Made of Sterner Stuff.

• Contour integration and “polylogarithmics” yield:

Theorem (Reduction Theorem for 0 ≤ τ ≤ 2π )

For n, k such that n− k ≥ 2, we have

ζ(k, {1}n)−k−2∑j=0

(−iτ)j

j!Lik−j,{1}n (e

iτ)

=(−i)k−1

(k − 2)!

(−1)n

(n + 1)!

n+1∑r=0

r∑m=0

(n + 1

r

)(r

m

)(i

2

)r(−π)r−mLs

(k+m−2)n+k−(r−m)

(τ).

where Li2+k−j,{1}n−k−2(eiτ ) is a harmonic polylogarithm and

ζ(n− k, {1}k) is an Euler-Zagier sum.⊙AS shows an implementation (Thursday, ISSAC).

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Ls(k)n (τ) is Made of Sterner Stuff.

• Contour integration and “polylogarithmics” yield:

Theorem (Reduction Theorem for 0 ≤ τ ≤ 2π )

For n, k such that n− k ≥ 2, we have

ζ(k, {1}n)−k−2∑j=0

(−iτ)j

j!Lik−j,{1}n (e

iτ)

=(−i)k−1

(k − 2)!

(−1)n

(n + 1)!

n+1∑r=0

r∑m=0

(n + 1

r

)(r

m

)(i

2

)r(−π)r−mLs

(k+m−2)n+k−(r−m)

(τ).

where Li2+k−j,{1}n−k−2(eiτ ) is a harmonic polylogarithm and

ζ(n− k, {1}k) is an Euler-Zagier sum.⊙AS shows an implementation (Thursday, ISSAC).

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Ls(k)n

(π3

): A small miracle occurs: e−i

π3 = ei

π3 .

The Reduction Theorem now lets us find all values of Ls(k)n

(π3

)and so of µk(1 + x+ y∗):

Example (Values of Lsn (π/3))

Ls2

(π3

)= Cl2

(π3

)− Ls3

(π3

)=

7

108π3

Ls4

(π3

)=

1

2π ζ(3) +

9

2Cl4

(π3

)−Ls5

(π3

)=

1543

19440π5 − 6 Gl4,1

(π3

)Ls6

(π3

)=

15

2π ζ(5) +

35

36π3ζ(3) +

135

2Cl6

(π3

)−Ls7

(π3

)=

74369

326592π7 +

15

2πζ(3)2 − 135 Gl6,1

(π3

)J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

A Result for General τ

• An illustration of results produced by our programs (see alsoAS’s ISSAC talk):

Example (For 0 ≤ τ ≤ 2π)

Ls(1)4 (τ) = 2ζ(3, 1)− 2 Gl3,1 (τ)− 2τ Gl2,1 (τ)

+1

4Ls

(3)4 (τ)− 1

2π Ls

(2)3 (τ) +

1

4π2 Ls

(1)2 (τ)

=1

180π4 − 2 Gl3,1 (τ)− 2τ Gl2,1 (τ)

− 1

16τ4 +

1

6πτ3 − 1

8π2τ2.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

A Result for General τ

• An illustration of results produced by our programs (see alsoAS’s ISSAC talk):

Example (For 0 ≤ τ ≤ 2π)

Ls(1)4 (τ) = 2ζ(3, 1)− 2 Gl3,1 (τ)− 2τ Gl2,1 (τ)

+1

4Ls

(3)4 (τ)− 1

2π Ls

(2)3 (τ) +

1

4π2 Ls

(1)2 (τ)

=1

180π4 − 2 Gl3,1 (τ)− 2τ Gl2,1 (τ)

− 1

16τ4 +

1

6πτ3 − 1

8π2τ2.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Irreducibility and Binomial Sums

Example (The first presumably irreducible value for π/3)

Gl4,1

(π3

)=

∞∑n=1

∑n−1k=1

1k

n4sin(nπ

3

)=

3341

1632960π5 − 1

πζ2(3)− 3

∞∑n=1

1(2nn

)n6

while always

Ls(1)n+2

(π3

)=

n!(−1)n+1

2n

∞∑k=1

1

kn+2(

2kk

) .• Alternating binomial sums come from imaginary values of τ

via log sinh integrals at ρ = 1+√

52 .

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Irreducibility and Binomial Sums

Example (The first presumably irreducible value for π/3)

Gl4,1

(π3

)=

∞∑n=1

∑n−1k=1

1k

n4sin(nπ

3

)=

3341

1632960π5 − 1

πζ2(3)− 3

∞∑n=1

1(2nn

)n6

while always

Ls(1)n+2

(π3

)=

n!(−1)n+1

2n

∞∑k=1

1

kn+2(

2kk

) .• Alternating binomial sums come from imaginary values of τ

via log sinh integrals at ρ = 1+√

52 .

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

First EvaluationLet

µk(1 + x+ y∗ + z∗) := µ(1 + x+ y1 + z1, . . . , 1 + x+ yk + zk).(20)

Theorem

For all positive integers k, we have

µk(1 + x+ y∗ + z∗) = − 1

πk+1

∫ π

0

(θ log

(2 sin

θ

2

)− Cl2 (θ)

)kdθ

Then

µ1(1 + x+ y∗ + z∗) = − 2

π2Ls

(1)3 (π) =

7

2

ζ(3)

π2,

µ2(1 + x+ y∗ + z∗) = − 1

π3Ls

(2)5 (π) +

π2

90=

4

π2Li3,1(−1) +

7

360π2.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Two More Evaluations: with Kummer-type logarithmsLet

λn(x) := (n− 2)!

n−2∑k=0

(−1)k

k!Lin−k(x) logk |x|+ (−1)n

nlogn |x|,

so that

λ1

(12

)= log 2, λ2

(12

)=

1

2ζ(2), λ3

(12

)=

7

8ζ(3),

and λ4

(12

)is the first to reveal the presence of Lin

(12

). From the

value of W′′4 (0) we derive:

Theorem

µ2(1 + x+ y + z) =12

π2λ4

(12

)− π2

5

µ(1 + x, 1 + x, 1 + x+ y + z) =4

3π2λ5

(12

)− 3

4ζ(3) +

31

16π2ζ(5).

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

KLO’s Mahler Measures

Theorem (Hypergeometric forms for µn(1 + x+ y))

For complex |s| < 2, we may write

∞∑n=0

µn(1 + x+ y)sn

n!=

√3

2π3s+1 Γ(1 + s

2 )2

Γ(s+ 2)3F2

(s+22 , s+2

2 , s+22

1, s+32

∣∣∣∣14)(21)

=

√3

π

(3

2

)s+1 ∫ 1

0

z1+s2F1

(1+ s

2 ,1+s2

1

∣∣∣∣ z24 )√

1− z2dz.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Evaluation of µn(1 + x+ y) Requires a Taylor Expansion

Consider

3F2

(ε+2

2 , ε+22 , ε+2

2

1, ε+32

∣∣∣∣14)

=

∞∑n=0

αnεn. (22)

Indeed, from (21) and Leibniz’ rule we have

µn(1 + x+ y) =

√3

n∑k=0

(n

k

)αkβn−k (23)

where βk is defined by

3ε+1 Γ(1 + ε2)2

Γ(ε+ 2)=

∞∑n=0

βnεn.

Note, as above, that βk is easy to compute.J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Faa di Bruno’s Formula

We can now read off the terms αn of the ε-expansion:

Theorem (For n = 0, 1, 2, . . .)

Let Ak,j :=∑2j−1m=2

2(−1)m+1−1mk

. Then

[εn] 3F2

(ε+22 , ε+2

2 , ε+22

1, ε+32

∣∣∣∣14)

= (−1)n∞∑j=1

2

j

1(2jj

) ∑ n∏k=1

Amkk,jmk!kmk

(24)

where we sum over all m1, . . . ,mn with m1 + 2m2 + . . .+ nmn = n.

Proof.

Equation (24) follows from (21) on using Faa di Bruno’s formula for then-th derivative of the composition on two functions via Pochhammernotation.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Faa di Bruno’s Formula

We can now read off the terms αn of the ε-expansion:

Theorem (For n = 0, 1, 2, . . .)

Let Ak,j :=∑2j−1m=2

2(−1)m+1−1mk

. Then

[εn] 3F2

(ε+22 , ε+2

2 , ε+22

1, ε+32

∣∣∣∣14)

= (−1)n∞∑j=1

2

j

1(2jj

) ∑ n∏k=1

Amkk,jmk!kmk

(24)

where we sum over all m1, . . . ,mn with m1 + 2m2 + . . .+ nmn = n.

Proof.

Equation (24) follows from (21) on using Faa di Bruno’s formula for then-th derivative of the composition on two functions via Pochhammernotation.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Faa di Bruno’s Formula

We can now read off the terms αn of the ε-expansion:

Theorem (For n = 0, 1, 2, . . .)

Let Ak,j :=∑2j−1m=2

2(−1)m+1−1mk

. Then

[εn] 3F2

(ε+22 , ε+2

2 , ε+22

1, ε+32

∣∣∣∣14)

= (−1)n∞∑j=1

2

j

1(2jj

) ∑ n∏k=1

Amkk,jmk!kmk

(24)

where we sum over all m1, . . . ,mn with m1 + 2m2 + . . .+ nmn = n.

Proof.

Equation (24) follows from (21) on using Faa di Bruno’s formula for then-th derivative of the composition on two functions via Pochhammernotation.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Davydychev and Kalmykov’s Binomial Sums Yield:

Example

µ1(1 + x+ y) =3

2πLs2

(2π

3

)µ2(1 + x+ y) =

3

πLs3

(2π

3

)+π2

4

µ3(1 + x+ y)?=

6

πLs4

(2π

3

)− 9

πCl4

(π3

)− π

4Cl2

(π3

)− 1

2ζ(3).

As we had obtained by other methods. Also PSLQ then finds:

πµ4(1 + x+ y)?= 12 Ls5

(2π

3

)− 49

3Ls5

(π3

)+ 81 Gl4,1

(2π

3

)+ 3π2 Gl2,1

(2π

3

)+ 2ζ(3) Cl2

(π3

)+ πCl2

(π3

)2− 29

90π5.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Davydychev and Kalmykov’s Binomial Sums Yield:

Example

µ1(1 + x+ y) =3

2πLs2

(2π

3

)µ2(1 + x+ y) =

3

πLs3

(2π

3

)+π2

4

µ3(1 + x+ y)?=

6

πLs4

(2π

3

)− 9

πCl4

(π3

)− π

4Cl2

(π3

)− 1

2ζ(3).

As we had obtained by other methods. Also PSLQ then finds:

πµ4(1 + x+ y)?= 12 Ls5

(2π

3

)− 49

3Ls5

(π3

)+ 81 Gl4,1

(2π

3

)+ 3π2 Gl2,1

(2π

3

)+ 2ζ(3) Cl2

(π3

)+ πCl2

(π3

)2− 29

90π5.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Davydychev and Kalmykov’s Binomial Sums Yield:

Example

µ1(1 + x+ y) =3

2πLs2

(2π

3

)µ2(1 + x+ y) =

3

πLs3

(2π

3

)+π2

4

µ3(1 + x+ y)?=

6

πLs4

(2π

3

)− 9

πCl4

(π3

)− π

4Cl2

(π3

)− 1

2ζ(3).

As we had obtained by other methods. Also PSLQ then finds:

πµ4(1 + x+ y)?= 12 Ls5

(2π

3

)− 49

3Ls5

(π3

)+ 81 Gl4,1

(2π

3

)+ 3π2 Gl2,1

(2π

3

)+ 2ζ(3) Cl2

(π3

)+ πCl2

(π3

)2− 29

90π5.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Conclusion

We also have generalized arctangent forms, such as:

µ2(1 +x+ y) =24

5πTi3

(1√3

)+

2 log 3

πCl2

(π3

)− log2 3

10− 19π2

180.

1 We are still hunting for a complete accounting ofµn(1 + x+ y).

2 Our log-sine and MZV algorithms uncovered many errors inthe literature.

3 Automated simplification, validation and correction tools aremore and more important.

4 Thank you!

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Conclusion

We also have generalized arctangent forms, such as:

µ2(1 +x+ y) =24

5πTi3

(1√3

)+

2 log 3

πCl2

(π3

)− log2 3

10− 19π2

180.

1 We are still hunting for a complete accounting ofµn(1 + x+ y).

2 Our log-sine and MZV algorithms uncovered many errors inthe literature.

3 Automated simplification, validation and correction tools aremore and more important.

4 Thank you!

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Conclusion

We also have generalized arctangent forms, such as:

µ2(1 +x+ y) =24

5πTi3

(1√3

)+

2 log 3

πCl2

(π3

)− log2 3

10− 19π2

180.

1 We are still hunting for a complete accounting ofµn(1 + x+ y).

2 Our log-sine and MZV algorithms uncovered many errors inthe literature.

3 Automated simplification, validation and correction tools aremore and more important.

4 Thank you!

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Conclusion

We also have generalized arctangent forms, such as:

µ2(1 +x+ y) =24

5πTi3

(1√3

)+

2 log 3

πCl2

(π3

)− log2 3

10− 19π2

180.

1 We are still hunting for a complete accounting ofµn(1 + x+ y).

2 Our log-sine and MZV algorithms uncovered many errors inthe literature.

3 Automated simplification, validation and correction tools aremore and more important.

4 Thank you!

J.M. Borwein Mahler Measures

Page 174: Mahler Measures,Short Walksand Log-sine Integrals · 48. Log-sine Integrals 9. Multiple Polylogarithms 10. Log-sine Integrals 11. Random Walks 16. Mahler Measures 17. Carlson’s

4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

Conclusion

We also have generalized arctangent forms, such as:

µ2(1 +x+ y) =24

5πTi3

(1√3

)+

2 log 3

πCl2

(π3

)− log2 3

10− 19π2

180.

1 We are still hunting for a complete accounting ofµn(1 + x+ y).

2 Our log-sine and MZV algorithms uncovered many errors inthe literature.

3 Automated simplification, validation and correction tools aremore and more important.

4 Thank you!

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

π2 base 2

Base-64 digits of π2 beginning at position 10 trillion. The first run,which produced base-64 digits starting from position 1012 − 1,required an average of 253,529 seconds per thread, subdivided intoseven partitions of 2048 threads each, so the total cost was7 · 2048 · 253529 = 3.6× 109 CPU-seconds. Each rack of the IBMBlue Gene P system features 4096 cores, so the total cost is 10.3“rack-days.” The second run, which produced base-64 digitsstarting from position 1012, took the same time (within a fewminutes). The two resulting base-8 digit strings are

75|60114505303236475724500005743262754530363052416350634|573227604

xx|60114505303236475724500005743262754530363052416350634|220210566

(each pair of base-8 digits corresponds to a base-64 digit). Herethe digits in agreement are delimited by |. Note that 53consecutive base-8 digits (159 binary digits) are in agreement.

J.M. Borwein Mahler Measures

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4. Introduction17. Short Random Walks

41. Multiple Mahler Measures48. Log-sine Integrals

49. Sasaki’s Mahler Measures52. Log-sine-cosine integrals57. Three Cognate Evaluations59. KLO’s Mahler Measures63. Conclusion

π2 base three

Base-729 digits of π2 beginning at position 10 trillion. Now thetwo runs each required an average of 795,773 seconds per thread,similarly subdivided as above, so that the total cost was 6.5× 109

CPU-seconds, or 18.4 “rack-days” for each run.The two resulting base-9 digit strings are

001|12264485064548583177111135210162856048323453468|10565567635862

xxx|12264485064548583177111135210162856048323453468|04744867134524

(each triplet of base-9 digits corresponds to one base-729 digit).Note here that 47 consecutive base-9 digits (94 base-3 digits) arein agreement.

J.M. Borwein Mahler Measures