PPT-Efficiency of Algorithms

Readings SG Ch 3 Chapter Outline Attributes of Algorithms Measuring Efficiency of Algorithms Simple Analysis of Algorithms Polynomial vs Exponential Time Algorithms Efficiency of Algorithms . Efficiency techniques are frequently documented as follows Describe an efficiency technique Demonstrate the technique with examples The drawback to this approach is that it can be difficult for SAS software users to determine when to apply the techn 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. Graph Algorithms. Many problems are naturally represented as graphs. Networks, Maps, Possible paths, Resource Flow, etc.. Ch. 3 focuses on algorithms to find connectivity in graphs. Ch. 4 focuses on algorithms to find paths within graphs. 1. Evolutionary Algorithms. CS 478 - Evolutionary Algorithms. 2. Evolutionary Computation/Algorithms. Genetic Algorithms. Simulate “natural” evolution of structures via selection and reproduction, based on performance (fitness). Reading Assignment. Mandatory: Chapter 2 – Sections 2.3 and 2.4. 2. Relations. Relating sets. 3. Objectives. At the end of this section, you will be able to:. Define relations and represent them in two ways (sets and graphical). Instructor: Arun Sen. Office: BYENG . 530. Tel: 480-965-6153. E-mail: asen@asu.edu. Office Hours: . MW 3:30-4:30 or by appointment. TA: . TBA. Office. : TBA. Tel: . TBA. E-mail: . TBA. Office Hours. : . Problem - a well defined task.. Sort a list of numbers.. Find a particular item in a list.. Find a winning chess move.. Algorithms. A series of precise steps, known to stop eventually, that solve a problem.. Alper Sarikaya. Advised by Prof. Dave Bacon. Computer Science & Engineering. Chemistry. University of Washington. Undergraduate Research Symposium. May 15, . 2009. Quantum Computing Theory Group: . Kevin Bronson. Brenda Tubana. Brian Arnall. Kevin F. Bronson, . Adi. . Malapati. . Jason . Nusz. , Jeff Johnson. . Texas A & M . AgriLife. . Res, and Texas Tech . Univ. . Lubbock, TX . Improved Strategies for Nitrogen Management in Subsurface Drip Irrigated Cotton. Chapter 4. Adapted from Pearson Education, Inc.. 1. Contents. Adapted from Pearson Education, Inc.. Motivation. Measuring an Algorithm’s Efficiency. Counting Basic Operations. Best, Worst, and Average Cases. Kevin Bronson. Brenda Tubana. Brian Arnall. Kevin F. Bronson, . Adi. . Malapati. . Jason . Nusz. , Jeff Johnson. . Texas A & M . AgriLife. . Res, and Texas Tech . Univ. . Lubbock, TX . Improved Strategies for Nitrogen Management in Subsurface Drip Irrigated Cotton. 10 Bat Algorithms Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier, 2014 The bat algorithm (BA) is a bio-inspired algorithm developed by Xin-She Yang in 2010. 10.1 Echolocation of Bats The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand DAIRY PLANT MANAGEMENT (DTT-421). A K JHA. INTRODUCTION. Operation of a dairy plant in a highly effective manner is possible only when all factors involved . are synchronized. . . It is difficult to Perfection .