PPT-Analysis of Algorithms:

Methods and Examples CSE 2320 Algorithms and Data Structures Vassilis Athitsos University of Texas at Arlington 1 Why Asymptotic Behavior Matters Asymptotic behavior The behavior of a function as the input approaches infinity. for Linear Algebra and Beyond. Jim . Demmel. EECS & Math Departments. UC Berkeley. 2. Why avoid communication? (1/3). Algorithms have two costs (measured in time or energy):. Arithmetic (FLOPS). Communication: moving data between . Some of the fastest known algorithms for certain tasks rely on chance. Stochastic/Randomized Algorithms. Two common variations. Monte Carlo. Las Vegas. We have already encountered some of both in this class. 1. Brute Force. A straightforward approach, usually based . directly. on the problem’s . statement and . definitions. . of the concepts involved. Examples – based directly on definitions:. Computing . Lecture 6. The maximum contiguous subsequence sum problem.. 8/25/2009. 1. ALG0183 Algorithms & Data Structures by Dr Andy Brooks. Weiss Chapter 5.3. There are many algorithms to solve this problem and their performances vary dramatically.. Lecture 18. The basics of graphs.. 8/25/2009. 1. ALG0183 Algorithms & Data Structures by Dr Andy Brooks. Watch out for self-loops in graphs.. 8/25/2009. ALG0183 Algorithms & Data Structures by Dr Andy Brooks. George Caragea, and Uzi Vishkin. University of Maryland. 1. Speaker. James Edwards. It has proven to be quite . difficult. to obtain significant performance improvements using current parallel computing platforms.. Keyang. He. Discrete Mathematics. Basic Concepts. Algorithm . – . a . specific set of instructions for carrying out a procedure or solving a problem, usually with the requirement that the procedure terminate at some point. BIT 1003- Presentation 4. An algorithm is a method for solving a class of problems. . While computer scientists think a lot about algorithms, the term applies to any method of solving a particular type of problem. . Aleksandar. R. . Mihajlovic. Technische. . Uni. versität München. mihajlovic@mytum.de. +49 176 673 41387. +381 63 183 0081. 1. Overview . Explain input data based imputation algorithm categorization scheme. 1. Brute Force. A straightforward approach, usually based . directly. on the problem’s . statement and . definitions. . of the concepts involved. Examples – based directly on definitions:. Computing . Algorithm. Input. Output. 1. Analysis of Algorithms. How long does this take to open 1) know 2) don’t know. . Analysis of Algorithms. 2. If know combination O(n) . where n is number of rings. . If the alphabet is size m, O(nm). 1. Brute Force. A straightforward approach, usually based . directly. on the problem’s . statement and . definitions. . of the concepts involved. Examples – based directly on definitions:. Computing . 1. Brute Force. A straightforward approach, usually based . directly. on the problem’s . statement and . definitions. . of the concepts involved. Examples – based directly on definitions:. Computing . 1. Decrease-and-Conquer. Reduce . original problem . instance to . smaller . instance . of the same problem. Solve smaller . instance. Extend solution . of smaller . instance . to obtain solution to original instance.