algorithms solutions ch 01 oct2010

CHAPTER 1

The Role of Algorithms in Computing

October 2010

1.1 Algorithms

Definition

An algorithm is any well-defined computational procedure that

takes some value, or set of values, as input and produces some

value, or set of values, as output. An algorithm is thus a

sequence of computational steps that transform the input into

the output.

We can also view an algorithm as a tool for solving a well-

specified computational problem. The statement of the

problem specifies in general terms the desired input/output

relationship.

The algorithm describes a specific computational

procedure for achieving that input/output relationship.

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Example

One might need to sort a sequence of numbers into

nondecreasing order. Here is how we formally define the

sorting problem:

Input: A sequence of n numbers ‹a1, a2, …, an›.

Output: A permutation (reordering) ‹a′1, a′2, …, a′n› of the input

sequence such that a′1≤ a′2 ≤ … ≤ a′n.

Given the input sequence ‹31, 41, 59, 26, 41, 58›, a sorting

algorithm returns as output the sequence ‹26, 31, 41, 41, 58,

59›. Such an input sequence is called an instance of the sorting

problem.






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In general, an instance of a problem consists of the input

(satisfying whatever constraints are imposed in the problem

statement) needed to compute a solution to the problem.

Sorting is a fundamental operation in computer science,

and as a result a large number of good sorting algorithms have

been developed. Which algorithm is best for a given application

depends on:

- The number of items to be sorted.

- The extent to which the items are already sorted.

- Possible restrictions on the item values.

- The kind of storage device to be used: main memory,

disks, or tapes.

An algorithm is said to be correct if, for every input

instance, it halts with the correct output.

We say that a correct algorithm solves






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October 2010

the given computational problem.

An incorrect algorithm might not

halt at all on some input instances,

or it might halt with an answer

other than the desired one.

Contrary to what one might expect, incorrect algorithms

can sometimes be useful, if their error rate can be controlled.

Ordinarily, however, we shall be concerned only with correct

algorithms.

An algorithm can be specified in English, as a computer

program, or even as a hardware design.

The only requirement is that the specification must provide a

precise description of the computational procedure to be

followed.






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October 2010

Exercise 1.1-1

Give a real-word example in which one of the following

computational problems appears: sorting.

Solution

Real world examples:

1- In web service if we need to see the mails by date we use

sorting.

2- A simple example is when we are working with a PC, on

the desktop we can arrange the icons by name, type, size.

These are all using sorting.






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October 2010

Exercise 1.1-3

Select a data structure that you have seen previously, and

discuss its strengths and limitations.

Solution

Data Structure : Arrays.

Strengths:

1- Arrays permit efficient ( constant time , O(1) ).

2- Most appropriate for sorting a fixed amount of data which

will be accessed in an unpredictable fashion.

3- Iterating through an array has good locality of reference.

Limitations:

1- It is not efficient for insertion and deletion of elements

(O(n)).

2- It can not grow or shrink dynamically.






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October 2010

Exercise 1.1-5

Come up with a real-word problem in which only the best

solution will do. Then come up with one in which a solution

that is “approximately” the best is good enough.

Solution

1- Hard real time systems: These are typically found

interacting at a low level with physical hardware in

embedded systems. In which time is critical.

If we have an algorithm of time complexity O(1) needed,

we need to use only this algorithm. This is the best

solution.

2- Soft real time systems: These are not critical time

requirements we can use on desired algorithms with

desired running time. There’s no need for best solution.

Approximately best solution can also be used.






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October 2010

1.2 Algorithms as a technology

Suppose computers were infinitely fast and computer

memory was free. Would you have any reason to study

algorithms? The answer is yes, because you would still like to

demonstrate that your solution method terminates and does so

with the correct answer.

If computers were infinitely fast, any correct method for

solving a problem would do. You would probably want your

implementation to be within the bounds of good software

engineering practice (i.e., well designed and documented),

but you would most often use whichever method was the

easiest to implement.

Of course, computers may be fast, but they are not

infinitely fast. And memory may be cheap, but it is not free.

Computing time is therefore a bounded resource, and so is كـل هدية اندينار على التنبيه بالبريد SMS برسالةالموقع مذكرات ب خطـأعنـد أو





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space in memory. These resources should be used wisely, and

algorithms that are efficient in terms of time or space will help

you do so.

Efficiency

Algorithms devised to solve

the same problem often differ

dramatically in their efficiency.

These differences can be much

more significant than differences

due to hardware and software.

Example

Two algorithms for sorting:

First: insertion sort, takes time roughly equal to c1n2 to

sort n items, where c1 is a constant that does not depend on n.

That is, it takes time roughly proportional to n2.






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Second: merge sort, takes time roughly equal to c2n lg n,

where lg n stands for log2 n and c2 is another constant that also

does not depend on n.

Note: Insertion sort usually has a smaller constant factor

than merge sort, so that c1 < c2. We shall see that the constant

factors can be far less significant in the running time than the

Continue

dependence on the input size n. Where merge sort has a factor

of lg n in its running time, insertion sort has a factor of n, which

is much larger. Although insertion sort is usually faster than

merge sort for small input sizes, once the input size n becomes

large enough, merge sort’s advantage of lg n vs. n will more

than compensate for the difference in constant factors.

No matter how much smaller c1 is than c2, there will always

be a crossover point beyond which merge sort is faster.

Assume that a faster computer (computer A) is running

insertion sort against a slower computer (computer B) running

merge sort.






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A: executes one billion instructions per second with an

Insertion code that requires 2n2 instructions to sort n numbers.

(c1 = 2).

B: executes only ten million instructions per second with a

Merge sort code using a high-level language with

an inefficient compiler, which takes 50n lg n

instructions to sort n numbers (c2 =50).

Continue

To sort one million numbers:

We notice that computer B runs 20 times faster than computer

A.

The advantage of merge sort is even more pronounced

when we sort ten million numbers: Insertion sort takes

approximately 2.3 days, merge sort takes under 20 minutes.






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In general, as the problem size increases, so

does the relative advantage of merge sort.

Exercise 1.2-1

Give an example of an application that requires algorithmic

content at the application level, and discuss the function of the

algorithms involved.

Solution

Simple web-based applications require a degree of algorithmic

content on their own.






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For example, Consider a web based service that determines

how to travel from one location to another, its implementation

would rely on fast hardware.

A graphical user interface, wide-area networking.

And also possible on object orientations.

However it would also require algorithms for certain

operations, such as finding routes (probably using shortest path

algorithm), rendering maps, and interpolating

addresses.

Exercise 1.2-2

Suppose we are comparing implementations of insertion sort

and merge sort on the same machine.

For inputs of size n, merge sort runs in 64n lg n steps, while

insertion sort runs in 8n2 steps. For which values on n does

insertion sort beat merge sort ?






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Solution

Insertion sort beats merge sort when

8n2 < 64n lg n

n < 8 lg n

2n/8 < n

This is true for 2 ≤ n ≤ 43 (found by using a calculator).

Rewrite merge sort to use insertion sort for

input size 43 or less in order to improve

the running time.

Exercise 1.2-3

What is the smallest value of n such that an algorithm whose

running time is 100n2 runs faster than an algorithm whose

running time is 2n on the same machine?






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Solution

The smallest value of n is 15. Where algorithm having running

time 100n2 runs faster than algorithm having running time 2n on

the same machine.

If

2n 100(n2)

n = 0 1 0 (where ‘0’ not considered)

n = 1 2 100

n = 2 4 400

. . .

. . .

n = 13 8192 16900

n = 14 16384 19600

n = 15 32,768 22500

Thus n = 15 is the smallest value at which 100 n2 runs faster

than 2n.






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algorithms solutions ch 01 oct2010

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