PDF-Lecture Selection deterministic randomized nding the median in linear time

1 Overview Given an unsorted array how quickly can one 64257nd the median element Can one do it more quickly than by sorting This was an open question for some time solved a64259rmatively in 1972 by Manuel Blum Floyd Pratt Rivest and Tarjan In this l. Sx Qx Ru with 0 0 Lecture 6 Linear Quadratic Gaussian LQG Control ME233 63 brPage 3br LQ with noise and exactly known states solution via stochastic dynamic programming De64257ne cost to go Sx Qx Ru We look for the optima under control A straightforward solution using two heaps permits adding elements in log comparisons with the current median always available We show how to reduce the expected number of comparisons for adding an element to 2 1 while preserving the worstcase bou Determinism and Randomness . Classical physics is deterministic!. If you know where you started you know where you are going. Randomness:. Quantum randomness is truly random and unpredictable. A lot of randomness is actually complexity and uncertainty. 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 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. Valerio Lucarini. valerio.lucarini@zmaw.de. Meteorologisches. . Institut. , . Klimacampus. , University of Hamburg. Dept. of Mathematics and Statistics, University of Reading. 1. Budapest,September. August Shi. , Alex Gyori, Owolabi Legunsen, Darko Marinov. 4/12/2016. ICST 2016. Chicago, Illinois. CCF-1012759. , CCF-1409423, . CCF-1421503, CCF-1439957. Example Code and Test. 2. public. . class. Theory. Just last . week. : . CMU . poker AI . player. . Libratus. . beats top human poker . players. in . heads. up . no. -limit Texas . Hold’em. . A monumental . achievement. ! (. Compare. to . c.n. .). L14. Glazer and Rubinstein (ECMA 2004). Glazer and Rubinstein (TE 2006). . Persuasion game. State space finite with aspect. Action space . Annealing . Dimension Reduction. and Biology. Indiana University. Environmental Genomics. April 20 2012. Geoffrey Fox. gcf@indiana.edu. . . http://www.infomall.org. . http://www.futuregrid.org. . 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. . Grigory. . Yaroslavtsev. (Indiana University, Bloomington). http://grigory.us. with . Sampath. . Kannan. (U. Pennsylvania),. Elchanan. . Mossel. (MIT) and . Swagato. . Sanyal. (NUS). -Sketching. Individual series: . The calculation of Median involves two basic steps (. i. ) location of median class and (ii) finding out its value.. The median class in individual series is [ (n+1)/2]. th. item.. . Norm Problems. and. Linear Programming. Syllabus. Lecture 01 Describing Inverse Problems. Lecture 02 Probability and Measurement Error, Part 1. Lecture 03 Probability and Measurement Error, Part 2 .