PPT-Randomized Algorithms

CS648 Lecture 4 Linearity of Expectation with applications Most important tool for analyzing randomized algorithms 1 RECAP from the last lecture 2 Random variable Definition A random variable defined over a probability space . 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 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. 1. , Dragi Kocev. 2. , . Suzana Lo. skovska. 1. , . Sašo Džeroski. 2. 1. Faculty of Electrical Engineering and Information Technologies, Department of Computer Science, Skopje, Macedonia. . 2. . CS648. . Lecture 17. Miscellaneous applications of . Backward analysis. 1. Minimum spanning tree. 2. Minimum spanning tree. . 3. b. a. c. d. h. x. y. u. v. 18. 7. 1. 19. 22. 10. 3. 12. 3. 15. 11. 5. 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 . Complexity of Voting Manipulation Revisited . b. ased on joint work with . Svetlana . Obraztsova. . (NTU/PDMI). and. . Noam . Hazon. . (CMU). Edith Elkind. . (Nanyang. Technological University, Singapore. Data Collection: . Experiments and Observational Studies. 1/23/12. Association. versus Causation. Confounding Variables. Observational Studies . vs. Experiments. Randomized Experiments. Section 1.3. 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, . 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: . Gertler. , P. J.; Martinez, S., . James . Aspnes. , Yale. Keren Censor-Hillel, Technion. 1. Snapshot Objects. p. 1. p. 2. p. n. …. …. update(. v. ). scan. 2. update your location. r. ead all locations. Model. 3. System of . n. . 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. Holger Thiele, . MD. o. n behalf of the CULPRIT-SHOCK Investigators. Disclosure Statement of Financial Interest. Grant/Research . Support. Consulting Fees/Honoraria. Major Stock Shareholder/Equity. Royalty Income. 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