PPT-Algorithms Lecture 20 Review: NP

Recall the definition of NP L NP if there exists an efficient verification algorithm V such that If x L there is a witnessproof w such that Vx w 1 If x L then for all proofs w we have Vx w 0. 409 The Behavior of Algorithms in Practice 2142 Lecture3 Lecturer Dan Spielman Scribe Arvind Sankar 1 Largest singular value In order to bound the condition number we need an upper bound on the largest singular va C Berkeley CS270 Algorithms Lecture 15 Professor Vazirani and Professor Rao Scribe Anupam Last revised Lecture 15 1 Streaming Algorithms Frequent Items Recall the streaming setting where we have a data stream x CS 477/677. Instructor: Monica Nicolescu. Lecture 2. CS 477/677 - Lecture 2. 2. Algorithm Analysis. The amount of resources used by the algorithm. Space. Computational time. Running time:. The number of primitive operations (steps) executed before termination. Presented by: Steve Shin. March 26, 2014. Background. Hometown: St. Louis. Graduated: May 2013. Integrated Master of Accounting Program. CPA Exam Study Period: Jun 2013 – Oct 2013. Exam Order & Dates. 1.204 Lecture 10 K k ( i t l b d t i ) K napsac k ( cap it a l b u d ge ti ng ) Job scheduling  Greedy method • Local im p p rov l in man y cases w h ere • Objecti v es 1 G52CON Lecture 5: Algorithms for Mutual Exclusion IOutline of this lecture CS 477/677. Instructor: Monica Nicolescu. Lecture . 13. CS 477/677 - Lecture 13. Midterm Exam. Tuesday, . March 8 . in . classroom. 75 minutes. Exam structure:. TRUE/FALSE questions. short questions on the topics discussed in class. CS 477/677. Instructor: Monica Nicolescu. Lecture . 4. CS 477/677 - Lecture 4. 2. Recurrences and Running Time. Recurrences arise when an algorithm contains recursive calls to itself. What is the actual running time of the algorithm?. CompSci. 590.03. Instructor: . Ashwin. . Machanavajjhala. 1. Lecture 3 : 590.03 Fall 12. Announcements. Project ideas are posted on the site. . You are welcome to send me (or talk to me about) your own ideas.. Let's first look at the . tests for 1 search. :. N. lg. 2. N. 8. 3. 16. 4. 1M. 20. 1G. 30. …. …. 64. 6. 32. 5. 1024. 10. 3. Lecture 9: Algorithm Analysis. Now consider multiple searches. Let's say for example I need to do 1 million searches of 1 million items. Instructor: Monica . Nicolescu. Lecture 4. CS 477/677 - Lecture 4. 2. Methods for Solving Recurrences. Iteration method. Substitution method. Recursion tree method. Master method. CS 477/677 - Lecture 4. We have discussed two classes of cryptographic assumptions. Factoring-based (factoring, RSA assumptions). Dlog. -based (. dlog. , CDH, and DDH assumptions). In two classes of groups. A. ll these problems are believed to be “hard,” i.e., to have no polynomial-time algorithms. Cyclic group G of order q with generator g.  G.  . G = {g. 0. , g. 1. , …, g. q-1. }. For any h .  G, define . log. g. h .  {0, …, q-1} as. . log. g. h = x  . Econ 340, Deardorff, Lecture 24: Review. 2. Lecture 24 Outline. For each lecture:. Outline. Major questions. Lists of. Terms. Acronyms (most are really Initialisms). Clicker questions. Especially on graphs.