Randomized Algorithms Randomized Algorithms CS648 Lecture 1 1.
Randomized Algorithms Kyomin Jung KAIST Applied Algorithm Lab Jan 12, WSAC 2010 1.
LEAST MEAN-SQUARE (LMS) ADAPTIVE FILTERING. Steepest Descent The update rule for SD is where or SD is a deterministic algorithm, in the sense that p and.
1 The Fortuna PRNG Niels Ferguson. 2 The problem We need to make “random” choices in cryptographic protocols. Computers are deterministic. Standard “random”
Beating Brute Force Search for Formula SAT and QBF SAT Rahul Santhanam University of Edinburgh.
Some Recent Results in Secure Pseudorandom Number Generation Berry Schoenmakers Joint work with Andrey Sidorenko and (partly) with Reza Rezaeian Farashahi.
1 Slides by Iddo Tzameret and Gil Shklarski. Adapted from Oded Goldreich’s course lecture notes by Erez Waisbard and Gera Weiss.
Pseudorandom Bit Generation Artur Gadomski Piero Giammarino Henrik Goldman Massimo Giulio Caterino.
The Equivalence between Static (rigid) and Kinematic (flexible, mobile) Systems through the Graph Theoretic Duality Dr. Offer Shai Tel-Aviv University.
Planning to Learn with a Knowledge Discovery Ontology Monika Žáková, Petr Křemen, Filip Železný (Czech Technical University, Prague) Nada Lavrač (Institute.
Binary Self - Stabilization By: Sukumar Ghosh Presented By: Ilan Cohen.
CS151 Complexity Theory Lecture 1 March 30, 2004.
