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Chapter 4 Algorithmic Foundation of Computer Science

Chapter 4 Algorithmic Foundation of Computer Science. What is an algorithm?. Example:. ingredients. software. hardware. Baking a cake. oven baker. recipe. cake. Software : algorithm. What is an algorithm?. Formal definitions:

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Chapter 4 Algorithmic Foundation of Computer Science

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  1. Chapter 4Algorithmic Foundation of Computer Science

  2. What is an algorithm? • Example: ingredients software hardware Baking a cake oven baker recipe cake • Software : algorithm

  3. What is an algorithm? • Formal definitions: • A sequence of operations that can solve a specified problem • A sequence of computational steps that transform the input into the output • A tool for solving a well-specified computational problem • A well-ordered collection of unambiguous and effectively computable operations that when executed produces a result and halts in a finite amount of time • Algorithmics: • The area of human study, knowledge, and expertise that concerns algorithms.

  4. History • 400 and 300 B.C. : • Greek mathematician: Euclid. • GCD (greatest common divisor) • The first non-trivial algorithm • Algorithms came from: • Persian mathematician: Mohammed al-Khowarizmi • When written in Latin: Algorismus • Key person : Alan Turing • English mathematician • Turing Machine: Finite Automata • Develop some results of the theory of algorithms, that concern the capabilities and limitations of machine-executable algorithms • Turing Award: • Nobel Prize of Computer Science

  5. Problem and Instance • Problem: • sorting problem: • sorting a sequence of numbers into nondecreasing order • input : A sequence of n number <a1,a2,...,an> • Output: A permutation (reordering) <a1',a2', ..., an'> of input sequence such that a1'<a2'< ... < an'. • Example: <31,41,59,26,20> => <20,26,31,41,59> • Instance : • a particular input sequence of a problem • Correct algorithm : • for every input sequence, the algorithm halts with the correct output.

  6. Why study algorithms? • More needs of high speed computations • How to accomplish that? • High speed computers • Highly efficient algorithms Note: High speed computers High speed computations Quicksort on PC/XT T Insertion sort on VAX 8800 N T : time N : # of data elements

  7. How to solve problems? • Algorithmic methods: • Getting it done methodically • Correctness of algorithms: • Getting it done right • Prove the correctness of algorithms • How? • By induction ... • Efficiency of algorithms: • Getting it done cheaply or quickly • Inefficiency and Intractability: • You can't always get it done cheaply • Noncomputability and Undecidability • Sometimes you can't get it done at all

  8. Performance of algorithms • How to determine the performance of an algorithm ? • depends on the size of input. • use special notations to denote the growth rate. • How to distinguish the easy problems and the difficult problems? • the complexity of problems • easy problem • a problem which can be solved by a polynomial time algorithm. • difficult problem • a problem which can only be solved by some exponential time algorithms. • NP-Complete problems.

  9. Difficult problems • Partition problem: • Input : S = {1, 7, 10, 9, 5, 8, 3, 13} • Output : S1 and S2 s.t. sum of S1 = sum of S2. • Example: S1 = { 1, 10, 9, 8 } S2 = { 7, 5, 3, 13 } • Traveling salesperson problem: • Find a minimum length tour 4 3 3 3 2 5 1 8 6

  10. Easy problems • Minimum spanning tree: • Find a tree with minimum length • One-center problem: • Find a smallest circle which can cover all points

  11. Analysis of algorithms • How to analyze? • Empirical approach • actually running time. • Theoretical approach • determining mathematically the quantity of resources needed by each algorithm as a function of the size of the instances considered. • How to measure? • Use a particular step or an elementary operation. • Examples: • What measurement? • Use O-notation, -notation and -notation. Operation Problem Comparison Multiplication Comparison/ Data Movement Find x in a list of names Multiply two matrices Sort n integers

  12. Complexity • Question: • Let A1 and A2 be two algorithms that solve the same problem. Let the time complexity of A1 and A2 be O(n2) and O(n) respectively. • Would the program for A2 run faster than that of A1 ? • Answer: • Not exactly! • Example: • A1 : n2 • A2 : 100n Note: 100n > n2, for n < 100.

  13. Complexity • How Important Is Order ? • Time Complexity Functions problem size 10 102 103 104 3.3 6.6 10 13.3 10 102 103 104 0.33*102 0.7*103 104 1.3*105 1024 1.3*1030 >10100 >10100 3*106 >10100 >10100 >10100 lg n n nlg n 2n n! functions

  14. What complexity? • What complexity do we need? • Best-case complexity • Worst-case complexity • Average-case complexity • Definition: • Let Dn be the set of inputs of size n for the problem under consideration, and let I be an element of Dn. • Let t(I) be the number of basic operations performed by the algorithm on input I. • Worst-Case Complexity : W(n) • W(n) = max { t(I) | I  Dn} • Best-Case Complexity : B(n) • B(n) = min { t(I) | I  Dn} • Average-Case Complexity: A(n) • p(I) : probability that input I occurs. A(n) =  p(I) t(I) IDn

  15. Example of analysis • Sequential search: • Problem: Find x in list L. • Basic operation: Comparison of x with a list entry. • Analysis: • Best case: • B(n) = 1 • Worst case: • W(n) = n • Average case: Assume all elements are distinct. • Case 1: Suppose x is in L • Ii : represent the case where x appears in the i-th position in L. • t(I) : the # of comparisons. • p(Ii) : prob. that Ii occurred. • t(Ii) = i, for 1  i  n. n n => A(n) =  p(Ii)t(Ii) =  (1/n) i i=1 i=1 n = (1/n)  i = (1/n)(n(n+1)/2)=(n+1)/2 i=1

  16. =  (q/n)i + (1-q)n Example of analysis • Case 2: x si not in L • (n+1) inputs should be considered. • In+1 : represent the case where x is not in L. • q : prob. that x is in L. • p(Ii) = q/n, for 1 <= i <= n • p(In+1) = 1- q. n+1 A(n) =  p(Ii)t(Ii) i=1 n i=1 = (q/n)(n(n+1))/ 2+ (1-q)n = q((n+1)/2) + (1-q)n Note: When q=1, A(n) = (n+1)/2 When q=1/2, A(n) = (n+1)/4 + n/2

  17. Sorting problem • Sorting problem: • Input: a list of numbers • Output: increasing order • Example: 5, 7, 2, 8, 3 => 2, 3, 5, 7, 8 • Selection sort: 1. Get values for n and the n items 2. Set the marker for the unsorted section at the end of the list 3. Repeat steps 4 thru 6 until the unsorted section of the list is empty 4. Select the largest number in the unsorted section of the list 5. Exchange this number with the last number in the unsorted section 6. Move the marker for the unsorted section forward one position 7. Stop 5, 7, 2, 8, 3 | 5, 7, 2, 3 | 8 5, 3, 2 | 7, 8 2, 3 | 5, 7, 8 2 | 3, 5, 7, 8 | 2, 3, 5, 7, 8 # of comparisons: (n-1)+(n-2)+ ... + 2 + 1 = n*(n-1)/2

  18. Binary search < X X > X • Time: • # of comparisons: lg n

  19. Pattern matching • Pattern matching problem: • Input: a pattern P and a text T • Output: All occurrences of pattern P • Example: • Text: KLMNPAQKKAQJDS • Pattern: AQ • Time:# of comparisons: • Length of T : n • Length of P : m • Best case : O(n) • Worst case: O(m*n)

  20. When things get out of hand • Exponential algorithm: • An algorithm which needs O(2n) steps to solve the problem • Intractable problems: • those problems which have no polynomially bounded algorithm to solve the problem exists • Example: • Bin-packing problem: • Given an unlimited number of bins of volume 1 unit, and n objects, all of volume between 0.0 and 1.0, find the minimum number of bins needed to store the n objects. • Approximation algorithms: • provide a close approximation to the solution of the problem. • Example: Bin-packing problem • First-Fit

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