approximation techniques for special eigenvalue problems ... fileapproximation techniques for...

Technische Universitat Munchen

Approximation Techniques for SpecialEigenvalue Problems

Konrad Waldherr

June 2011

Joint work with Thomas Huckle

K. Waldherr: Approximation Techniques for Special Eigenvalue Problems

HDA 2011, Bonn, June 2011 1


Outline1 Problem Setting: Computation of Ground States

Physical ModelMathematical Model

2 Data-Sparse Representation Formats

3 Matrix Product StatesFormalismComputations with MPSUniqueness of MPSMinimization in terms of MPS

4 Subspace Methods in Terms of MPS

5 Numerical results

6 Conclusions




Problem: Computation of Ground StatesGiven:

• Physical system: 1D spin chain with N particles

1 2 3 4 5

• Interaction within the system (e.g. nearest-neighbor interaction)

1 2 3 4 5

• External interaction (e.g. exterior magnetic field)

1 2 3 4 5

Goal:• Find ground state, i.e. the state related to the smallest energy of

the system.




Mathematical Model• Any state of the system is represented by a vector x ∈ C2N

.• The physical system can be described by the HamiltonianH ∈ C2N×2N

.

• The Hamiltonian may be formulated as a weighted sum ofKronecker products of Pauli and identity matrices.

• Pauli matrices:

σx =

(0 11 0

), σy =

(0 −ii 0

), σz =

(1 00 −1

).

• Kronecker product:

A⊗B :=

a11 . . . a1n

.... . .

...am1 . . . amn

⊗B =

a11B . . . a1nB...

. . ....

am1B . . . amnB

• Then, the ground state corresponds to the eigenvector related

to the smallest eigenvalue.






.• The Hamiltonian may be formulated as a weighted sum of

Kronecker products of Pauli and identity matrices.• Pauli matrices:

σx =

(0 11 0

), σy =

(0 −ii 0

), σz =

(1 00 −1

).


A⊗B :=

a11 . . . a1n

.... . .

...am1 . . . amn

⊗B =

a11B . . . a1nB...

. . ....

am1B . . . amnB

• Then, the ground state corresponds to the eigenvector relatedto the smallest eigenvalue.






.• The Hamiltonian may be formulated as a weighted sum of

Kronecker products of Pauli and identity matrices.• Pauli matrices:

σx =

(0 11 0

), σy =

(0 −ii 0

), σz =

(1 00 −1

).


A⊗B :=

a11 . . . a1n

.... . .

...am1 . . . amn

⊗B =

a11B . . . a1nB...

. . ....

am1B . . . amnB

• Then, the ground state corresponds to the eigenvector related

to the smallest eigenvalue.




Example: Ising-type Hamiltonian

1 2 3 4 5

H = σz ⊗ σz ⊗ I ⊗ I ⊗ I

+ I ⊗ σz ⊗ σz ⊗ I ⊗ I+ I ⊗ I ⊗ σz ⊗ σz ⊗ I+ I ⊗ I ⊗ I ⊗ σz ⊗ σz+ σx ⊗ I ⊗ I ⊗ I ⊗ I+ I ⊗ σx ⊗ I ⊗ I ⊗ I+ I ⊗ I ⊗ σx ⊗ I ⊗ I+ I ⊗ I ⊗ I ⊗ σx ⊗ I+ I ⊗ I ⊗ I ⊗ I ⊗ σx





1 2 3 4 5

H = σz ⊗ σz ⊗ I ⊗ I ⊗ I+ I ⊗ σz ⊗ σz ⊗ I ⊗ I

+ I ⊗ I ⊗ σz ⊗ σz ⊗ I+ I ⊗ I ⊗ I ⊗ σz ⊗ σz+ σx ⊗ I ⊗ I ⊗ I ⊗ I+ I ⊗ σx ⊗ I ⊗ I ⊗ I+ I ⊗ I ⊗ σx ⊗ I ⊗ I+ I ⊗ I ⊗ I ⊗ σx ⊗ I+ I ⊗ I ⊗ I ⊗ I ⊗ σx





1 2 3 4 5

H = σz ⊗ σz ⊗ I ⊗ I ⊗ I+ I ⊗ σz ⊗ σz ⊗ I ⊗ I+ I ⊗ I ⊗ σz ⊗ σz ⊗ I

+ I ⊗ I ⊗ I ⊗ σz ⊗ σz+ σx ⊗ I ⊗ I ⊗ I ⊗ I+ I ⊗ σx ⊗ I ⊗ I ⊗ I+ I ⊗ I ⊗ σx ⊗ I ⊗ I+ I ⊗ I ⊗ I ⊗ σx ⊗ I+ I ⊗ I ⊗ I ⊗ I ⊗ σx





1 2 3 4 5

H = σz ⊗ σz ⊗ I ⊗ I ⊗ I+ I ⊗ σz ⊗ σz ⊗ I ⊗ I+ I ⊗ I ⊗ σz ⊗ σz ⊗ I+ I ⊗ I ⊗ I ⊗ σz ⊗ σz

+ σx ⊗ I ⊗ I ⊗ I ⊗ I+ I ⊗ σx ⊗ I ⊗ I ⊗ I+ I ⊗ I ⊗ σx ⊗ I ⊗ I+ I ⊗ I ⊗ I ⊗ σx ⊗ I+ I ⊗ I ⊗ I ⊗ I ⊗ σx





1 2 3 4 5

H = σz ⊗ σz ⊗ I ⊗ I ⊗ I+ I ⊗ σz ⊗ σz ⊗ I ⊗ I+ I ⊗ I ⊗ σz ⊗ σz ⊗ I+ I ⊗ I ⊗ I ⊗ σz ⊗ σz+ σx ⊗ I ⊗ I ⊗ I ⊗ I

+ I ⊗ σx ⊗ I ⊗ I ⊗ I+ I ⊗ I ⊗ σx ⊗ I ⊗ I+ I ⊗ I ⊗ I ⊗ σx ⊗ I+ I ⊗ I ⊗ I ⊗ I ⊗ σx





1 2 3 4 5

H = σz ⊗ σz ⊗ I ⊗ I ⊗ I+ I ⊗ σz ⊗ σz ⊗ I ⊗ I+ I ⊗ I ⊗ σz ⊗ σz ⊗ I+ I ⊗ I ⊗ I ⊗ σz ⊗ σz+ σx ⊗ I ⊗ I ⊗ I ⊗ I+ I ⊗ σx ⊗ I ⊗ I ⊗ I

+ I ⊗ I ⊗ σx ⊗ I ⊗ I+ I ⊗ I ⊗ I ⊗ σx ⊗ I+ I ⊗ I ⊗ I ⊗ I ⊗ σx





1 2 3 4 5

H = σz ⊗ σz ⊗ I ⊗ I ⊗ I+ I ⊗ σz ⊗ σz ⊗ I ⊗ I+ I ⊗ I ⊗ σz ⊗ σz ⊗ I+ I ⊗ I ⊗ I ⊗ σz ⊗ σz+ σx ⊗ I ⊗ I ⊗ I ⊗ I+ I ⊗ σx ⊗ I ⊗ I ⊗ I+ I ⊗ I ⊗ σx ⊗ I ⊗ I

+ I ⊗ I ⊗ I ⊗ σx ⊗ I+ I ⊗ I ⊗ I ⊗ I ⊗ σx





1 2 3 4 5

H = σz ⊗ σz ⊗ I ⊗ I ⊗ I+ I ⊗ σz ⊗ σz ⊗ I ⊗ I+ I ⊗ I ⊗ σz ⊗ σz ⊗ I+ I ⊗ I ⊗ I ⊗ σz ⊗ σz+ σx ⊗ I ⊗ I ⊗ I ⊗ I+ I ⊗ σx ⊗ I ⊗ I ⊗ I+ I ⊗ I ⊗ σx ⊗ I ⊗ I+ I ⊗ I ⊗ I ⊗ σx ⊗ I

+ I ⊗ I ⊗ I ⊗ I ⊗ σx





1 2 3 4 5

H = σz ⊗ σz ⊗ I ⊗ I ⊗ I+ I ⊗ σz ⊗ σz ⊗ I ⊗ I+ I ⊗ I ⊗ σz ⊗ σz ⊗ I+ I ⊗ I ⊗ I ⊗ σz ⊗ σz+ σx ⊗ I ⊗ I ⊗ I ⊗ I+ I ⊗ σx ⊗ I ⊗ I ⊗ I+ I ⊗ I ⊗ σx ⊗ I ⊗ I+ I ⊗ I ⊗ I ⊗ σx ⊗ I+ I ⊗ I ⊗ I ⊗ I ⊗ σx




Problem Setting• General formulation of the Hamiltonian H:

H =

M∑k=1

αkQ(k)1 ⊗ · · · ⊗Q(k)

N , Q(k)i ∈ {id, σx, σy, σz} ⊂ C2×2

• Variational ansatz: Minimization of the Rayleigh quotient:

minx 6=0

xHHx

xHx

• Problems:

• the vector space V = C2N

grows exponentially in N ,• the problem is not generic.

• Idea: Find data-sparse representation formats that allow toovercome the curse of dimensionality.




Problem Setting• General formulation of the Hamiltonian H:

H =

M∑k=1

αkQ(k)1 ⊗ · · · ⊗Q(k)

N , Q(k)i ∈ {id, σx, σy, σz} ⊂ C2×2

• Variational ansatz: Minimization of the Rayleigh quotient:

minx 6=0

xHHx

xHx

• Problems:• the vector space V = C2N

grows exponentially in N ,• the problem is not generic.

• Idea: Find data-sparse representation formats that allow toovercome the curse of dimensionality.




Data-sparse representation formats• The data-sparse format has to allow for

1. proper reproduction of the physical setting (e.g.nearest-neighbor interaction),

2. representations that are only polynomial in N ,3. good approximation properties,4. efficient evaluation of the Rayleigh quotient

minx∈U

xHHx

xHx(1)

=⇒ efficient computation of both Hx and yHx.

• Problem setting:• Not: Find a data-sparse format of a given data structure• But: Use the data-sparse format as an ansatz for the

minimization of the Rayleigh quotient (1).




Data-sparse representation formats• The data-sparse format has to allow for

1. proper reproduction of the physical setting (e.g.nearest-neighbor interaction),

2. representations that are only polynomial in N ,3. good approximation properties,4. efficient evaluation of the Rayleigh quotient

minx∈U

xHHx

xHx(1)

=⇒ efficient computation of both Hx and yHx.• Problem setting:

• Not: Find a data-sparse format of a given data structure• But: Use the data-sparse format as an ansatz for the

minimization of the Rayleigh quotient (1).




Tensor Decompositions• Any state x ∈ C2N

may be considered as a N th order tensor:

x = (xi1i2...iN )ij=0,1

i1 i2 iN

• Goal: Find convenient tensor decomposition as ansatz for (1)• Classical decomposition formats:

• Tucker decomposition,• Canonical decomposition.

• Physically motivated formats:• Matrix Product States (MPS) for 1D problems,• Projected Entangled Pair States (PEPS) for 2D problems.






x = (xi1i2...iN )ij=0,1

i1 i2 iN

• Goal: Find convenient tensor decomposition as ansatz for (1)

• Classical decomposition formats:• Tucker decomposition,• Canonical decomposition.







x = (xi1i2...iN )ij=0,1

i1 i2 iN

• Goal: Find convenient tensor decomposition as ansatz for (1)• Classical decomposition formats:

• Tucker decomposition,• Canonical decomposition.





Decompositions of a tensor• Tucker decomposition:

Not advantageous in our case of a binary tensor.

• Canonical decomposition (CP):

Allows generalizations in several ways: (see [Huckle 2011])• blockings and mixed blockings,• permutations.




Decompositions of a tensor• Tucker decomposition:

Not advantageous in our case of a binary tensor.• Canonical decomposition (CP):

Allows generalizations in several ways: (see [Huckle 2011])• blockings and mixed blockings,• permutations.




Matrix Product States (MPS)• Consider again the linear 1D spin chain.

b b b b b

site j

• Each site j is related to a matrix pair A(0)j , A

(1)j ∈ CDj×Dj+1

b b b b b

A(ij)j

• For i = (i1, i2, . . . , iN )2, the vector component xi is given by

xi = xi1i2...iN = tr(A

(i1)1 ·A(i2)

2 · · ·A(iN )N

)

b b b b b

ij−1 ij ij+1 ij+2 ij+3

mj−1 mj mj+1 mj+2

(1.X)


HDA 2011, Bonn, June 2011 10


Matrix Product States• Thus, the vector x may be written as

x =

2N∑i=1

xiei =∑

i1,...,ip

tr(A

(i1)1 ·A(i2)

2 · · ·A(iN )N

)︸︷︷︸

=xi=xi1,...,iN

ei1i2...iN︸︷︷︸=ei

.

• Binary version of the Tensor Train concept. (see [Oseledets andTyrtyshnikov, 2009])

• MPS representation requires 2ND2 instead of 2N entries. (2.X)• Any state can be represented exactly by an MPS (but at the cost

of exponentially growing matrix dimension D),• For approximation problems, D has to scale only polynomially.

(3.X)


HDA 2011, Bonn, June 2011 11



x =

2N∑i=1

xiei =∑

i1,...,ip

tr(A

(i1)1 ·A(i2)

2 · · ·A(iN )N

)︸︷︷︸

=xi=xi1,...,iN


.


• MPS representation requires 2ND2 instead of 2N entries. (2.X)

• Any state can be represented exactly by an MPS (but at the costof exponentially growing matrix dimension D),

• For approximation problems, D has to scale only polynomially.(3.X)


HDA 2011, Bonn, June 2011 11



x =

2N∑i=1

xiei =∑

i1,...,ip

tr(A

(i1)1 ·A(i2)

2 · · ·A(iN )N

)︸︷︷︸

=xi=xi1,...,iN


.


• MPS representation requires 2ND2 instead of 2N entries. (2.X)• Any state can be represented exactly by an MPS (but at the cost

of exponentially growing matrix dimension D),• For approximation problems, D has to scale only polynomially.

(3.X)


HDA 2011, Bonn, June 2011 11


Matrix Product StatesExample

• Consider the case N = 2 and D = 1:

ψ =

1∑i1,i2=0

(a

(i1)1 a

(i2)2

)︸︷︷︸

=xi=xi1i2

(ei1 ⊗ ei2)︸︷︷︸=ei=ei1i2

=

a

(0)1 a

(0)2

a(0)1 a

(1)2

a(1)1 a

(0)2

a(1)1 a

(1)2

!=

x1

x2

x3

x4

• For exact representation:

x1x4 = x2x3

• e1, e2, e3, e4 can be represented,• However, e1 + e4 = (1, 0, 0, 1)T cannot be represented.

• MPS are not closed under addition!


HDA 2011, Bonn, June 2011 12


Computations with MPS• Sum of two MPS vectors

x =∑

i1,...,iN

tr(A(i1)1 A

(i2)2 . . . A

(iN )N )ei1...iN

y =∑

i1,...,iN

tr(B(i1)1 B

(i2)2 . . . B

(iN )N )ei1...iN

x+ y =∑i1,...,iN

tr[(A

(i1)1 0

0 B(i1)1

)(A

(i2)2 0

0 B(i2)2

)︸︷︷︸

D′×D′

. . .

(A

(iN )N 0

0 B(iN )N

)]ei1...iN

• Summing MPS vectors leads to an augmentation of the matrixsizes D → D′.

• Use compression techniques to keep the size of the MPSmatrices constant.


HDA 2011, Bonn, June 2011 13


Computations with MPS• Inner products

xHy =∑

i1,...,iN

xi1,...,iN︷︸︸︷(tr(A

(i1)1 · · · A(iN )

N

))·

yi1,...,iN︷︸︸︷(tr(B

(i1)1 · · ·B(iN )

N

))=∑ij

∑kj

a(i1)1;k1,k2

· · · a(iN )N ;kN ,k1

∑mj

b(i1)1;m1,m2

· · · b(iN )N ;mN ,m1

Find an efficient ordering of the summations!• Matrix × vector

Hx =

(M∑k=1

αkQ(k)1 ⊗ · · · ⊗Q(k)

N

) ∑i1,...,iN

tr(A(i1)1 . . . A

(iN )N )ei1...iN

=

M∑k=1

y(k)MPS

=⇒ 4. XK. Waldherr: Approximation Techniques for Special Eigenvalue Problems

HDA 2011, Bonn, June 2011 14


Uniqueness of MPS• The MPS formalism is not unique:∑

i1,...,iN

tr(A

(i1)1 ·A(i2)

2 · · ·A(iN )N

)ei1,...,iN

=∑

i1,...,iN

tr(

(A(i1)1 M−1

2 )(M2A(i2)2 M−1

3 ) · · · (MNA(iN )N )

)ei1,...,iN

• Replace the matrices A(0)j , A

(1)j by parts of unitary matrices using

the SVD: (A

(0)j

A(1)j

)=

(U

(0)j

U(1)j

)ΣjVj .

• Multiply the ΣjVj parts on the right neighbour.• Replace all A matrices by U matrices, up to one.• This remaining factor is considered to be optimized.• Leads to better convergence properties and numerical stability.


HDA 2011, Bonn, June 2011 15



i1,...,iN

tr(A

(i1)1 ·A(i2)

2 · · ·A(iN )N

)ei1,...,iN

=∑

i1,...,iN

tr(

(A(i1)1 M−1

2 )(M2A(i2)2 M−1

3 ) · · · (MNA(iN )N )

)ei1,...,iN



the SVD: (A

(0)j

A(1)j

)=

(U

(0)j

U(1)j

)ΣjVj .

• Multiply the ΣjVj parts on the right neighbour.

• Replace all A matrices by U matrices, up to one.• This remaining factor is considered to be optimized.• Leads to better convergence properties and numerical stability.


HDA 2011, Bonn, June 2011 15



i1,...,iN

tr(A

(i1)1 ·A(i2)

2 · · ·A(iN )N

)ei1,...,iN

=∑

i1,...,iN

tr(

(A(i1)1 M−1

2 )(M2A(i2)2 M−1

3 ) · · · (MNA(iN )N )

)ei1,...,iN



the SVD: (A

(0)j

A(1)j

)=

(U

(0)j

U(1)j

)ΣjVj .

• Multiply the ΣjVj parts on the right neighbour.• Replace all A matrices by U matrices, up to one.• This remaining factor is considered to be optimized.

• Leads to better convergence properties and numerical stability.


HDA 2011, Bonn, June 2011 15



i1,...,iN

tr(A

(i1)1 ·A(i2)

2 · · ·A(iN )N

)ei1,...,iN

=∑

i1,...,iN

tr(

(A(i1)1 M−1

2 )(M2A(i2)2 M−1

3 ) · · · (MNA(iN )N )

)ei1,...,iN



the SVD: (A

(0)j

A(1)j

)=

(U

(0)j

U(1)j

)ΣjVj .

• Multiply the ΣjVj parts on the right neighbour.• Replace all A matrices by U matrices, up to one.• This remaining factor is considered to be optimized.• Leads to better convergence properties and numerical stability.


HDA 2011, Bonn, June 2011 15


Computation of the Ground StateHow to calculate the smallest eigenvalue of the Hamiltonian H?

1. Variational ansatz:

minxMPS

xHMPSHxMPS

xHMPSxMPS

• Find optimal matrix pairs A(ij)j for all sites j

• Use Alternating Least Squares: Keep all matrix pairs up toone fixed and optimize the remaining one

2. New idea: Use subspace iteration

minx∈Um

xHHx

xHx

• Find optimal solution in the subspace Um

• Um is defined by MPS vectors


HDA 2011, Bonn, June 2011 16


Minimization Algorithm in terms of MPS• Start with initial guesses for the A(ij)

j matrices.

• Replace A(ij)j by U (ij)

j up to A(i1)1 .

• Consider all U matrices as fixed and optimize X1 = A1:

minX1

(∑ij

tr(X

(i1)1 U

(i2)2 · · ·U(iN )

N

)ei

)H

H

(∑ij

tr(X

(i1)1 U

(i2)2 · · ·U(iN )

N

)ei

)(∑

ij

tr(X

(i1)1 U

(i2)2 · · ·U(iN )

N

)ei

)H(∑ij

tr(X

(i1)1 U

(i2)2 · · ·U(iN )

N

)ei

)

= minX1

X1HR1X1

X1HX1

with effective Hamiltonian R1

• =⇒ dense eigenvalue problem for the (2D2 × 2D2) matrix R1.• Orthogonalize the solution and continue with the right neighbor.

Modifications of this algorithm lead to the DMRG method.


HDA 2011, Bonn, June 2011 17


Subspace iteration for the smallest eigenvalue• Idea: Minimize the Rayleigh quotient xHHx

xHx over a subspace (!)Um := span{q1, . . . , qm}.

• Let Vm = (q1, . . . , qm) ∈ C2N×m

• The minimization over Um yields

minx∈Um\{0}

xHHx

xHx

x=Vmy= min

y∈Cm\{0}

(Vmy)HH(Vmy)

(Vmy)HVmy

= miny∈Cm\{0}

yH(V HmHVm

)y

yH (V HmVm) y

= miny∈Cm\{0}

yHAy

yHBy,

a generalized eigenvalue problem Ay = λminBy of size m×m.• x = Vmy or restart with q1 = Vmy

• How to choose an appropriate subspace Um?


HDA 2011, Bonn, June 2011 18




• Let Vm = (q1, . . . , qm) ∈ C2N×m


minx∈Um\{0}

xHHx

xHx

x=Vmy= min

y∈Cm\{0}

(Vmy)HH(Vmy)

(Vmy)HVmy

= miny∈Cm\{0}

yH(V HmHVm

)y

yH (V HmVm) y

= miny∈Cm\{0}

yHAy

yHBy,

a generalized eigenvalue problem Ay = λminBy of size m×m.

• x = Vmy or restart with q1 = Vmy



HDA 2011, Bonn, June 2011 18




• Let Vm = (q1, . . . , qm) ∈ C2N×m


minx∈Um\{0}

xHHx

xHx

x=Vmy= min

y∈Cm\{0}

(Vmy)HH(Vmy)

(Vmy)HVmy

= miny∈Cm\{0}

yH(V HmHVm

)y

yH (V HmVm) y

= miny∈Cm\{0}

yHAy

yHBy,

a generalized eigenvalue problem Ay = λminBy of size m×m.• x = Vmy or restart with q1 = Vmy



HDA 2011, Bonn, June 2011 18


Subspace iteration for the smallest eigenvalue• Choose the Krylov space Km(H, b) as subspace:

Um = Km(H, b) := span{b,Hb, . . . ,Hm−1b}

• This ansatz yields

A = V HmHVm =

bHHb bHH2b . . . bHHmbbHH2b bHH3b . . . bHHm+1b

......

. . ....

bHHmb bHHm+1b . . . bHH2m−1b

B = V HmVm =

bHb bHHb . . . bHHm−1bbHHb bHH3b . . . bHHmb

......

. . ....

bHHm−1b bHHmb . . . bHH2m−2b


HDA 2011, Bonn, June 2011 19


Subspace iteration in terms of MPS• b = bMPS andKm(H, bMPS) = span{bMPS , HbMPS , . . . ,H

m−1bMPS}:

• In principle the same proceeding, BUT

• Computation of the matrix vector products:

HjbMPS =

(M∑k=1

αkQ(k)1 ⊗ · · · ⊗Q(k)

N

)j

bMPS

=

M∑k1=1

· · ·M∑

kj=1

b(k1,...,kj)MPS

Number of summands grows dramatically!• Application of the back-transformation:

Vmymin =m∑j=1

ymin,jHj−1bMPS

does not lie in the MPS space.


HDA 2011, Bonn, June 2011 20



m−1bMPS}:• In principle the same proceeding, BUT


HjbMPS =

(M∑k=1

αkQ(k)1 ⊗ · · · ⊗Q(k)

N

)j

bMPS

=M∑

k1=1

· · ·M∑

kj=1

b(k1,...,kj)MPS


Vmymin =m∑j=1

ymin,jHj−1bMPS



HDA 2011, Bonn, June 2011 20





HjbMPS =

(M∑k=1

αkQ(k)1 ⊗ · · · ⊗Q(k)

N

)j

bMPS

=

M∑k1=1

· · ·M∑

kj=1

b(k1,...,kj)MPS

Number of summands grows dramatically!

• Application of the back-transformation:

Vmymin =m∑j=1

ymin,jHj−1bMPS



HDA 2011, Bonn, June 2011 20





HjbMPS =

(M∑k=1

αkQ(k)1 ⊗ · · · ⊗Q(k)

N

)j

bMPS

=

M∑k1=1

· · ·M∑

kj=1

b(k1,...,kj)MPS


Vmymin =

m∑j=1

ymin,jHj−1bMPS

does not lie in the MPS space.K. Waldherr: Approximation Techniques for Special Eigenvalue Problems

HDA 2011, Bonn, June 2011 20


Subspace iteration in terms of MPS• Solution: Application of a projection P onto the MPS space:

P :

M∑k=1

y(k)MPS 7→ argminxMPS

∥∥∥∥∥M∑k=1

y(k)MPS − xMPS

∥∥∥∥∥

• Solve the optimization problem

minA

∥∥∥∥∥∥∑

i1,...,iN

tr

(M∑k=1

(B(i1;k)1 · · ·B(iN ;k)

N )−A(i1)1 · · ·A(iN )

N

)ei1...iN

∥∥∥∥∥∥• Two possible projection methods:

• SVD-based truncation• ALS-based optimization

• Use ALS to find optimal matrix pair A(0)r , A

(1)r

• Forcing the derivative to be zero leads to a linear system.


HDA 2011, Bonn, June 2011 21



P :

M∑k=1


∥∥∥∥∥M∑k=1

y(k)MPS − xMPS

∥∥∥∥∥• Solve the optimization problem

minA

∥∥∥∥∥∥∑

i1,...,iN

tr

(M∑k=1

(B(i1;k)1 · · ·B(iN ;k)

N )−A(i1)1 · · ·A(iN )

N

)ei1...iN

∥∥∥∥∥∥

• Two possible projection methods:• SVD-based truncation• ALS-based optimization


(1)r



HDA 2011, Bonn, June 2011 21



P :

M∑k=1


∥∥∥∥∥M∑k=1

y(k)MPS − xMPS

∥∥∥∥∥• Solve the optimization problem

minA

∥∥∥∥∥∥∑

i1,...,iN

tr

(M∑k=1

(B(i1;k)1 · · ·B(iN ;k)

N )−A(i1)1 · · ·A(iN )

N

)ei1...iN

∥∥∥∥∥∥• Two possible projection methods:

• SVD-based truncation• ALS-based optimization


(1)r



HDA 2011, Bonn, June 2011 21


Numerical resultsIsing-type Hamiltonian, N = 10, dim = 2

1 5 10 15 20 25 30 35 40 45 5010

−6

10−4

10−2

100

102

Number of iterations

App

roxi

mat

ion

erro

r

Full vectorMPS, D=1MPS, D=2MPS, D=3MPS, D=4


HDA 2011, Bonn, June 2011 22



1 5 10 15 20 25 30 35 40 45 5010

−6

10−4

10−2

100

102


App

roxi

mat

ion

erro

r



HDA 2011, Bonn, June 2011 23



5 10 15 20 25 30 35 40 45 50110

−6

10−4

10−2

100

102


App

roxi

mat

ion

erro

r



HDA 2011, Bonn, June 2011 24



1 5 10 1510

−4

10−3

10−2

10−1

100

101

102


App

roxi

mat

ion

erro

r

MPS, D=2MPS, D=3MPS, D=4MPS, D=5


HDA 2011, Bonn, June 2011 25


Conclusions• Matrix Product States

• data-sparse representation format• represent ground states faithfully• DMRG in terms of MPS

• Subspace methods in terms of MPS• more flexibility (choice of subspace dimension)• approximation not only of the lowest eigenvalue• projection required to keep the number of summands limited• projection can even improve the approximation• convergence to DMRG solution• convergence properties of subspace iteration and

approximation properties of MPS

Thank you for your attention.


HDA 2011, Bonn, June 2011 26


Conclusions• Matrix Product States

• data-sparse representation format• represent ground states faithfully• DMRG in terms of MPS

• Subspace methods in terms of MPS• more flexibility (choice of subspace dimension)• approximation not only of the lowest eigenvalue• projection required to keep the number of summands limited• projection can even improve the approximation• convergence to DMRG solution• convergence properties of subspace iteration and

approximation properties of MPS

Thank you for your attention.K. Waldherr: Approximation Techniques for Special Eigenvalue Problems

HDA 2011, Bonn, June 2011 26

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