cse5311M1Fall09 - CSE5311 Design and Analysis of Algorithms...

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Unformatted text preview: CSE5311 Design and Analysis of Algorithms 8/24/2009 CSE5311 Fall 2009 M Kumar 1 What are algorithms? An algorithm is a precise and unambiguous specification of a sequence of steps that can be carried out to solve a given problem or to achieve a given condition. An algorithm is a computational procedure to solve a well defined computational problem. An algorithm accepts some value or set of values as input and produces a value or set of values as output. An algorithm transforms the input to the output. Algorithms are closely intertwined with the nature of the data structure of the input and output values. Data structures are methods for representing the data models on a computer whereas data models are abstractions used to formulate problems. 8/24/2009 CSE5311 Fall 2009 M Kumar 2 Where do we use Algorithms? Everyday Life Going from Point A to Point B A recipe for preparing a food item Decision making AI Databases Networks Multimedia Systems Bioinformatics Ant colonies Computer Science Biology Economics Marketing Running a Business Music Games Others ... please add CSE5311 Fall 2009 M Kumar 3 8/24/2009 Example Algorithms An algorithm to sort a sequence of natural numbers into non decreasing order Application : Lexicographical ordering An algorithm to find a shortest path from one node to another in a graph Application: Routing in computer networks An algorithm to find the best scheduling of events to resources Application: Lecture halls to courses An algorithm to recognize a substring in a string of letters Application: Word find in a text. 8/24/2009 CSE5311 Fall 2009 M Kumar 4 Reallife Examples Travelling sales person problem Google Probe, crawl, search, sort, rank.... Amazon Search, mine, match, rank.... Travelocity Search, mine, match, rank,... Netflix (http://netflix.com) New Releases, Classics, TV episodes and more on DVD Over 100,000 DVD titles. 100 shipping points nationwide and more than 95 percent of our members receive their DVDs in about one business day. 8/24/2009 CSE5311 Fall 2009 M Kumar 5 Problem 1 A man needs to transport a wolf, a goat and a cabbage across a river. The boat has room only for the man and one other item (either the wolf, the goat or the cabbage). In the absence of the man the wolf would eat the goat and the goat would eat the cabbage. Solve this problem for the man. 8/24/2009 CSE5311 Fall 2009 M Kumar 6 Problem 2 Four persons A,B, C, and D wish to cross a bridge. It is dark at night and they need to use the only flashlight in their possession, that has a battery life only 17 mins. A maximum of two people can cross the bridge at any given time. Each person walks at a different pace and a pair must walk at the slower person's pace. The times taken by the four persons (if allowed to cross individually) are given as: A 1 min; B 2 mins; C 5 mins; and D10 mins; 8/24/2009 CSE5311 Fall 2009 M Kumar 7 Problem 3 The town of Konigsberg (now Kaliningrad) lay on the banks and on two islands of the Pregel river. The city was connected by 7 bridges. The puzzle (as encountered by Leonhard Euler in 1736) : Whether it was possible to start walking from anywhere in town and return to the starting point by crossing all bridges exactly once. A Konigsberg bridges A C D D B C B 8/24/2009 CSE5311 Fall 2009 M Kumar 8 Course Description Design and Analysis of Algorithms is THE most important basic course in any graduate computer science and engineering curriculum. It is vital for every computer science student to be fluent with algorithms and their analysis. Students are encouraged to solve homework problems and discuss/solve problems in the class. Each student will be given one specific algorithm or problem to carry out an indepth study. Typically, this course should be taken in the very first semester of your graduate study because algorithms are used in Networks, Operating Systems, Databases, and other (including advanced) courses. 8/24/2009 CSE5311 Fall 2009 M Kumar 9 Course Objectives The objective of this course is to build a solid foundation of the most important fundamental subject in computer science. Creative thinking is essential to algorithm design. Algorithm analysis and verification demands sound mathematical acumen and programming skills. 8/24/2009 CSE5311 Fall 2009 M Kumar 10 Mode of Teaching The class meets twice a week (Mondays and Wednesdays 1:00 to 2:20pm). Power point slides and other lecture material will be used. At the end of each topic, students must attempt to solve exercise problems. There will be no specific text book for the class the instructor will provide comprehensive notes and references to relevant material. Exercise problems can be found on the course web page and in reference books. All students are expected to work on these problems and participate in the class discussions. The course on Algorithms is critical to your development as a computer scientist, a researcher, a creative thinker and/or a problem solver. algorithms are extensively used in databases, networks, artificial intelligence, bioinformatics, pervasive and mobile computing, robotics, security, architecture, all engineering and science disciplines, finance, management, music, biology and indeed in everyday life. You will be encouraged to think on your own and to discuss solutions with your peers. The course is not limited to any programming language. Students are strongly encouraged to use reference books and course material that will be available and updated from time to time on the course page. 8/24/2009 CSE5311 Fall 2009 M Kumar 11 Information Instructor Mohan Kumar 333 NH Email: [email protected] Phone: (817) 2723610 Class: Mon/Wed 1:00 to 2:20 PM 314 PH Office Hours Mon/Wed 2:30 to 4:00 PM Course site: http://crystal.uta.edu/~kumar/cse5311_09FLDAA GTA: TBA 8/24/2009 CSE5311 Fall 2009 M Kumar 12 Syllabus Review of Asymptotic Analysis and Growth of Functions; Trees, Heaps, and Graphs; and Recurrences. Greedy Algorithms: Minimum spanning tree, UnionFind algorithms, Kruskal's Algorithm, Clustering, Huffman Codes, and Multiphase greedy algorithms. Dynamic Programming: Shortest paths, negative cycles, matrix chain multiplications, sequence alignment, RNA secondary structure, application examples. Network Flow: Maximum flow problem, FordFulkerson algorithm, augmenting paths, Bipartite matching problem, disjoint paths and application problems. NP and Computational tractability: Polynomial time reductions; The Satisfiability problem; NPComplete problems; and Extending limits of tractability. Approximation Algorithms Local Search and Randomized Algorithms Applications of Algorithms, sample examples 8/24/2009 CSE5311 Fall 2009 M Kumar 13 Reference Books Class Notes, Power Point Slides, and Exercise Problems Mohan Kumar Algorithm Design Jon Kleinberg and va Tardos, Pearson AddisonWesley The Design and Analysis of Algorithms 1974 AV Aho, JE Hopcroft and JD Ullman, AddisonWesley Publishing Company Introduction to Algorithms: A Creative Approach, Reprinted 1989 Udi Manber, AddisonWesley Publishing Company Introduction to Algorithms, Second Edition, 2001 T Cormen, C E Leiserson, R L Rivest and C Stein McGraw Hill and MIT Press Graph Algorithms, 1979 Shimon Even, Computer Science Press The Design & Analysis of Algorithms, 2005 Anany Levitan, Addison Wesley The Art of Computer Programming, Vols. 1 and 3 Knuth, Addison Wesley Publishing Company 8/24/2009 CSE5311 Fall 2009 M Kumar 14 Assessment Quizzes and class participation: 40% The structure of the quizzes will be discussed in class, at least one week prior to the quiz. Quiz 1 (10%): September 9, 2009 Quiz 2 (10%): September 23, 2009 Quiz 3 (10%): October 07, 2009 Quiz 4 (10%): October 28, 2009 Final Exam (30 %): December 02, 2009 Lab Assignment: 30% Quizzes 1 thru 4 are of duration 60 minutes and the Final Exam is of duration 2 hours. 8/24/2009 CSE5311 Fall 2009 M Kumar 15 QUESTIONS? 8/24/2009 CSE5311 Fall 2009 M Kumar 16 Study of Algorithms Designing algorithms Expressing algorithms Algorithm Validation Algorithm Analysis Alternative techniques 8/24/2009 CSE5311 Fall 2009 M Kumar 17 Algorithms vs. Program Code Algorithms An algorithm, is an abstraction of an actual program is a computational procedure that terminates An algorithm is composed of a finite set of steps, each step may require one or more operations, each operation must be definite and effective Program Code A program is an expression of an algorithm in a programming language. A program code conforms to the dictates of policies and limitations of a programming language. 8/24/2009 CSE5311 Fall 2009 M Kumar 18 Presenting algorithms Description : The algorithm will be described in English, with the help of one or more examples Specification : The algorithm will be presented as pseudo code (We don't use any programming language) Validation : The algorithm will be proved to be correct for all problem cases Analysis: The running time or time complexity of the algorithm will be evaluated 8/24/2009 CSE5311 SPRING 2007 MKUMAR 19 Algorithms An algorithm is designed to solve a given problem An algorithm does not take into account the intricacies and limitations of any programming language. we are free to express ourselves when designing an algorithm. An algorithm should be unambiguous it should have precise steps An algorithm has three main components: input the algorithm itself and output. An algorithm will be implemented using a programming language An algorithm designer is like an architect while programmers are like masons, carpenters, plumbers etc.) 8/24/2009 CSE5311 Fall 2009 M Kumar 20 Algorithms (Contd.) The algorithms we design should be Simple Unambiguous (e.g. The students should understand algorithms the instructor gives in the class and the GTA should understand the algorithms students write in a test or exam) Feasible Should be implementable using a programming language and executable on a computer. Cost effective CPU time Memory used Communication Energy 8/24/2009 CSE5311 Fall 2009 M Kumar 21 Algorithm Design Abstract solution to the problem Algorithmic solution to problems are applicable to many applications Resource limitations and constraints Time Most common criteria Modern applications demand more computing power and time Memory Modern applications demand more computing power and time Data in main memory Energy Critical to battery operated devices Application requirements Input/output limitations Time, space 8/24/2009 CSE5311 Fall 2009 M Kumar 22 Algorithm Evaluation We evaluate the performance of algorithms based on time (CPUtime) and Space (semiconductor memory) Both are expensive The time taken to execute an algorithm is dependent on one or more of the following, number of data elements the degree of a polynomial the size of a file to be sorted the number of nodes in a graph Computer scientists should endeavour to minimize time, space and energy required. 8/24/2009 CSE5311 Fall 2009 M Kumar 23 Expressing Algorithms Procedure ALGO_X (A [1,...,n]) Input : unsorted array A Output : Sorted array A for i 1 to n1 small i; for j i+1 to n if A[j] < A[small] then small j; temp A[small]; A[small] A[i]; A[i] temp; end Procedure AlGO_Y (A[1,...,n],i,n) Input : Unsorted array A Output : Sorted array A while i < n do small i; for j i+1 to n if A[j] < A[small] then small j; temp A[small]; A[small] A[i]; A[i] temp; ALGO_Y(A,i+1,n) end end M Kumar 24 8/24/2009 CSE5311 Fall 2009 Analyzing Algorithms Procedure AlGO_Y (A[1,...,n],i,n) Input : Unsorted array A Output : Sorted array A while i < n do small i; for j i+1 to n if A[j] < A[small] then small j; temp A[small]; A[small] A[i]; A[i] temp; ALGO_Y(A,i+1,n) end We start with data size n Last line same algorithm recalled, but for data size n 1 The algorithm takes (n1) steps to find the smallest element in the array T(n) = T(n1) + b * n Steps for finding the smallest element plus swap 25 Recursive call for data size n 1 8/24/2009 CSE5311 Fall 2009 M Kumar Analysis (Contd.) T(n) = T(n1) + b. n (1) T(n1) = T(n2) + (n1)b (2) T(n2) = T(n3) + (n2) b (3) ... T(ni) = T(n(i+1)) + (ni)b (4) Using (2) in (1) T(n) = T(n2) + b [n+(n1)] = T(n3) + b [n+(n1)+(n2) = T(n(n1)) + b[n+(n1)+(n2) + . . . +(n(n2))] T(n) = O(n2) 8/24/2009 CSE5311 Fall 2009 M Kumar 26 Asymptotic Notations Onotation Asymptotic upper bound A given function f(n), is O (g(n)) if there exist positive constants c and n0 such that 0 f(n) c g(n) for all n n0. O (g(n)) represents a set of functions, and O (g(n)) = {f(n): there exist positive constants c and n0 such that 0 f(n) c g(n) for all n n0}. 8/24/2009 CSE5311 SPRING 2007 MKUMAR 27 O Notation f(n), is O (g(n)) if there exist 40 35 30 25 20 15 10 5 0 1 8/24/2009 positive constants c and n0 such that 0 f(n) c g(n) for all n n0. f(n) = 2n+6 cg(n) = 4n c=4 2 3 4 5 6 7 8 9 n0 = 3.5 28 CSE5311 SPRING 2007 MKUMAR 45000 40000 35000 30000 25000 20000 15000 10000 5000 0 1 10 100 1000 10000 log n n n logn n^2 2^n 8/24/2009 CSE5311 SPRING 2007 MKUMAR 29 notation Asymptotic lower bound A given function f(n), is (g(n)) if there exist positive constants c and n0 such that 0 c g(n) f(n) for all n n0. (g(n)) represents a set of functions, and (g(n)) = {f(n): there exist positive constants c and n0 such that 0 c g(n) f(n) for all n n0} 8/24/2009 CSE5311 SPRING 2007 MKUMAR 30 notation Asymptotic tight bound A given function f(n), is (g(n)) if there exist positive constants c1, c2,and n0 such that 0 c1g(n) f(n) c2 g(n) for all n n0. (g(n)) represents a set of functions, and (g(n)) = {f(n): there exist positive constants c1, c2, and n0 such that 0 c1g(n) f(n) c2 g(n) for all n n0. O, , and correspond (loosely) to "", "", and "=". 8/24/2009 CSE5311 SPRING 2007 MKUMAR 31 Running Times and Space How many times each statement is executed? Are there loops in the algorithm? Is the algorithm iterative, repetitive, recursive etc. How much memory is used in executing the algorithm? 8/24/2009 CSE5311 Fall 2009 M Kumar 32 Algorithm complexity log 2n n=10 n=20 n=50 n=100 n=103 n=104 n=105 n=106 < 1 sec < 1 sec < 1 sec < 1 sec < 1 sec < 1 sec < 1 sec < 1 sec n < 1 sec < 1 sec < 1 sec < 1 sec < 1 sec < 1 sec < 1 sec 1 sec nlog2n < 1 sec < 1 sec < 1 sec < 1 sec < 1 sec < 1 sec 2 secs 20 secs n2 < 1 sec < 1 sec < 1 sec < 1 sec 1 sec 2 min 3 hours 12 days n3 < 1 sec < 1 sec < 1 sec 1 sec 18min 12 days 32 yrs 31710 yrs 2n < 1 sec 18 min 36 yrs 1017 yrs n! 4 secs 1025 yrs Processor performing 1 million highlevel instructions per second J. Kleinberg and E. Tardos, Algorithm Design, Addison Wesley, 2005 8/24/2009 CSE5311 Fall 2009 M Kumar 33 Constant Time Constant number of statements e.g., Let X = 4; Y = 6; if A[j] < A[small] then A[j] = SMALL The complexity (or running time) is O(1) 8/24/2009 CSE5311 Fall 2009 M Kumar 34 Logarithmic Time Divide and conquer algorithm Problem divided into two or more equal parts and each part solved recursively Binary Search Tree T (n) = c T (n/2) + O(1) Time to solve problem of size n is equal to time to solve problem of size n/2 (multiplied by a constant) PLUS constant time. 8/24/2009 CSE5311 Fall 2009 M Kumar 35 Linear Time The running time increases linearly with the size of the problem Computing the minimum of n data elements Merging two sorted lists O(n log2n) time algorithms Merge sort Quick sort Heap sort 8/24/2009 CSE5311 Fall 2009 M Kumar 36 Quadratic Time There are n points in a plane. If each point is specified by its (x,y) coordinates, find the closest pair of points. A brute force algorithm Dmin = For each point pi (xi,yi) for each point pj (xj,yj) such that ij compute Dij = [ (xixj)2 +(yiyj)2 ] If Dij < Dmin then Dij =Dmin End End What is the complexity of the algorithm? n(n1)/2 computational steps Quadratic time 8/24/2009 CSE5311 Fall 2009 M Kumar 37 Polynomial Time Problems that can be solved in polynomial time Algorithms when implemented, can be executed in polynomial time O(nk) 8/24/2009 CSE5311 Fall 2009 M Kumar 38 Beyond Polynomial Time Some problems cannot be solved in polynomial time There are NO known polynomial solutions for these problems Traveling Salesperson is a classic example of such a problem We will study such problems and approximate solutions to these problems 8/24/2009 CSE5311 Fall 2009 M Kumar 39 Recursive Algorithms A recursive function is one that is called from within its own body. The call be direct or indirect F calls itself; F1 calls F2, F2 calls F1. Fact(n) Begin If n 1 then Fact (n) =1; else Fact(n) = n*Fact(n1); End INDUCTION BASIS 8/24/2009 CSE5311 Fall 2009 M Kumar 40 Applications of recursion The Towers if Hanoi problem consists of three pegs A, B, and C, and n rings of varying size. Initially the rings are stacked on peg A in order of decreasing size, the largest ring at the bottom. The problem is to move the rings from peg A to peg B one at a time in such a way that no ring is ever placed on a smaller ring. Peg C may be used for temporary storage of rings. Write a recursive algorithm to solve this problem. What is the execution time of your algorithm in terms of the number of times a ring is moved? ... 8/24/2009 CSE5311 Fall 2009 M Kumar 41 Recursive Solution The problem Move n rings from A to B, using C as auxiliary if n > 1 Begin move recursively (n1) rings from A to C, using B as auxiliary move largest disk from A to B move recursively (n1) disks from C to B, using A as auxiliary End Time taken T (1) = 1 T (n) = 2T (n - 1) + 1 8/24/2009 CSE5311 Fall 2009 M Kumar 42 Switches 8/24/2009 CSE5311 Fall 2009 M Kumar 43 What is the recursion? Define? 8/24/2009 CSE5311 Fall 2009 M Kumar 44 Exercise Problems 1. Write a recursive algorithm to find the maximum of n real numbers in an array A [0 .. n1]. What is the complexity of your algorithm? 2. Derive an expression to find the sum of the first n squares, where n is a positive integer. Provide a proof for the sum using induction. Write an algorithm to find the sum of the first n squares. What is the complexity of the algorithm? 3. The input is a set S containing n real numbers, and a real number x. a. Design an algorithm to determine whether there are two elements of S whose sum is exactly x. The algorithm should run in O(n log n) time. b. Suppose now that the set S is given in a sorted order. Design an algorithm to solve the above problem in time O (n). 4. Given an array of integers A[1..n], such that, for all i, 1 i n, we have |A[i] A[i+1]| 1. Let A[1] = x and A[n] = y, such that x < y. Design an efficient search algorithm to find j such that A[j] = z for a given value z, x z y. What is the maximal number of comparisons to z that your algorithm makes? 8/24/2009 CSE5311 Fall 2009 M Kumar 45 Exercise Problems(Contd.) The Towers if Hanoi problem consists of three pegs A, B, and C, and n rings of varying size. Initially the rings are stacked on peg A in order of decreasing size, the largest ring at the bottom. The problem is to move the rings from peg A to peg b one at a time in such a way that no ring is ever placed on a smaller ring. Peg C may be used for temporary storage of rings. Write a recursive algorithm to solve this problem. What is the execution time of your algorithm in terms of the number of times a ring is moved? 8/24/2009 CSE5311 Fall 2009 M Kumar 46 Compare the following pairs of functions in terms of order of magnitude. In each case, say whether f(n) = O(g(n), f(n) = (g(n)), and/or f(n) = (g(n)) a. b. c. d. e. f. f(n) 100n +log n log n n2/log n (log n)log n n n 2n g(n) n + (log n)2 log(n2) n(log n)2 n/log n (log n)5 3n 8/24/2009 CSE5311 FALL 2008 MKUMAR 47 ...
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