PPT-Randomized Algorithms CS648

Lecture 2 Randomized Algorithm for Approximate Median Elementary Probability theory 1 Randomized Monte Carlo Algorithm for approximate median 2 This lecture was delivered at slow pace and its flavor was that of a tutorial . 005 p0005 p0005 p0013 p0013 p0013 25 25 25 20 20 20 15 15 15 10 10 10 5 5 5 0 0 0 5 5 5 01 01 01 2345678 2345678 2345678 Change in UDysRS Score LS Mean SE Change in UDysRS Score LS Mean SE Change in UDysRS Score LS Mean SE Weeks Weeks Weeks Placeb Impact Evaluation Methods for Policy Makers. This material constitutes supporting material for the "Impact Evaluation in Practice" book. This additional material is made freely but please acknowledge its use as follows: . Adversarial Environments. Andreas . Krause. Joint work with . Daniel . Golovin. and . Alex Roper. International Joint Conference on Artificial Intelligence 2011. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . CS648. . Lecture 3. Two fundamental problems. Balls into bins. Randomized Quick Sort. Random Variable and Expected . value. 1. Balls into BINS. Calculating probability of some interesting events. 2. CS648. . Lecture 15. Randomized Incremental Construction . (building the background). 1. Partition Theorem. A set of events . ,…,. . defined over a probability space (. ,. P. ) is said to induce a partition of . Michael Ben-Or. The Hebrew University. Michael Rabin’s Birthday Celebration. Randomized Protocols. Power of Randomization. Exponential . speedup for known algorithms. Complexity . – The jury is still out . CS648. . Lecture 6. Reviewing the last 3 lectures. Application of Fingerprinting Techniques. 1-dimensional Pattern matching. . Preparation for the next lecture.. . 1. Randomized Algorithms . discussed till now. CS648. . Lecture . 25. Derandomization. using conditional expectation. A probability gem. 1. Derandomization. using . conditional expectation. 2. Problem 1. : Large cut in a graph. Problem:. Let . of a graph. Spyros Angelopoulos*. Christoph . Dürr. *. Thomas . Lidbetter**. *. Sorbonne Universités. , UPMC . Univ. Paris 06, CNRS, LIP6, Paris, . France. **Department of Mathematics, London School of Economics, . Quicksort. . [CLRS, kapitel 7]. Gerth Stølting Brodal. Sandsynligheden for at slå krone. 1/2. Quicksort:. Sorter . A. [. p. ... r. ]. Hoare, 1961. Worst-case tid . O(. n. 2. ). A. p. r. x. A. p. r. Neighborhood. Hill Climbing. : Sample p points randomly in the neighborhood of the currently best . solution; determine the best solution of the n sampled points. If it is better than the . current solution, make it the new current solution and continue the search; otherwise, . Lower Bounds, and Pseudorandomness. Igor Carboni Oliveira. University of Oxford. Joint work with . Rahul Santhanam. (Oxford). 2. Minor algorithmic improvements imply lower bounds (Williams, 2010).. NEXP. 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.. 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