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Home , Analysis of algorithms

Design and Analysis of Chapter 1

CSCE 310J: Data Structures & Algorithms CSCE 310J: Data Structures & Algorithms

I Giving credit where credit is due: • Most of the lecture notes are based on the slides from Introduction the Textbook’s companion website – http://www.aw.com/cssuport/ Dr. Steve Goddard • I have modified them and added new slides [email protected]

http://www.cse.unl.edu/~goddard/Courses/CSCE310J

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What is a ? Features of Algorithm

I “Besides merely being a finite set of rules that gives a I An algorithm is a sequence of unambiguous sequence of operations for solving a specific type of instructions for solving a problem, i.e., for problem, an algorithm has the five important features” obtaining a required output for any legitimate [Knuth1] input in a finite amount of time. – finiteness (otherwise: computational method) » termination – definiteness » precise definition of each step – input (zero or more) – output (one or more) – effectiveness » “Its operations must all be sufficiently basic that they can in principle be done exactly and in a finite length of time by someone using pencil and paper” [Knuth1]

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Computer Algorithm or Program? Notion of algorithm

I A computer algorithm is problem • a detailed step-by-step method for solving a problem using a computer. algorithm I A program is • an of one or more algorithms. input“computer” output

Algorithmic solution

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Example of : sorting Selection Sort

I Statement of problem: I Input: array a[1],…,a[n] • Input: A sequence of n numbers

´ ´ ´ I Output: array a sorted in non-decreasing order • Output: A reordering of the input sequence ´ ´ so that a i ≤ a j whenever i < j

I Algorithm: I Instance: The sequence <5, 3, 2, 8, 3>

I Algorithms: • Selection sort for i=1 to n • swap a[i] with smallest of a[i],…a[n] • • (many others) • see also pseudocode, section 3.1 Design and Analysis of Algorithms - Chapter 1 7 Design and Analysis of Algorithms - Chapter 1 8

Some Well-known Computational Problems Basic Issues Related to Algorithms

I Sorting I How to design algorithms I Searching I Shortest paths in a graph I How to express algorithms I Minimum spanning tree I Proving correctness I Primality testing I Traveling salesman problem I Efficiency I Knapsack problem • Theoretical analysis I Chess • Empirical analysis I Towers of Hanoi I Program termination I Optimality

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Algorithm design strategies Analysis of Algorithms

I How good is the algorithm? • Correctness • Time efficiency I Brute force I Greedy approach • Space efficiency

I Divide and conquer I Dynamic programming I Does there exist a better algorithm? I Decrease and conquer I Backtracking and Branch and bound • Lower bounds • Optimality I Transform and conquer I Space and time tradeoffs

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Correctness

I Termination I – Well-founded sets: find a quantity that is never negative and I that always decreases as the algorithm is executed – For iterative algorithms: sums I Partial Correctness – For recursive algorithms: recurrence relations – For recursive algorithms: induction – For iterative algorithms: axiomatic semantics, loop invariants

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What is an algorithm? Why study algorithms?

I Recipe, process, method, technique, procedure, routine,… I Theoretical importance with following requirements: 1. Finiteness • the core of I terminates after a finite number of steps 2. Definiteness I Practical importance I rigorously and unambiguously specified 3. Input • A practitioner’s toolkit of known algorithms I valid inputs are clearly specified 4. Output • Framework for designing and analyzing algorithms for new I can be proved to produce the correct output given a valid input problems 5. Effectiveness I steps are sufficiently simple and basic

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