PPT-1 Lecture 17: Sublinear-time algorithms
Author : danika-pritchard | Published Date : 2018-11-05
COMS E69989 F15 Administrivia Plan Admin My office hours after class CSB517 Plan Finalize embeddings Sublineartime algorithms Projects Scriber 2 Embeddings of
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1 Lecture 17: Sublinear-time algorithms: Transcript
COMS E69989 F15 Administrivia Plan Admin My office hours after class CSB517 Plan Finalize embeddings Sublineartime algorithms Projects Scriber 2 Embeddings of various metrics . 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. Talya Eden, . Tel Aviv . University. Amit Levi, . University of Waterloo. Dana . Ron, . Tel Aviv . University. C. . Seshadhri. , . UC Santa Cruz. Counting Triangles. Basic graph-theoretic algorithmic . 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.. Huijia. Lin (USB), . Rafael Pass . (Cornell). Karn. . Seth . (Cornell -> Google). Sid . Telang. (Cornell -> Google). IO. Plethora of Applications. For example: SW14, BCP14, BZ14, GGHR14, BGL. 2. . Turing machine. . RAM (. Figure . ). . Logic circuit model. . RAM . (Random Access Machine). Operations . supposed to be executed in one unit time. (1). . Control operations such as. Dana Ron . Tel-Aviv University. ADGA, October 2015. Efficient (Centralized) . Algorithms. Usually, when we say that an algorithm is . efficient . we mean that it runs in time . polynomial. in the input size . Lecture . 10: . Sublinear. Algorithm. Zhu Han. University of Houston. Thanks for Professor Dan Wang’s slides. 1. outline. Motivations. Inequalities and classifications . Examples. Applications. 2. Ashish Goel. Joint work with Peter Lofgren; Sid Banerjee; C . Seshadhri. 1. Personalized PageRank. 2. Assume a directed graph with . n. nodes and . m. edges. Motivation: Personalized Search. . 3. Motivation: Personalized Search. Talya Eden, . Tel Aviv . University. Amit Levi, . University of Waterloo. Dana . Ron, . Tel Aviv . University. C. . Seshadhri. , . UC Santa Cruz. Counting Triangles. Basic graph-theoretic algorithmic . Lecture . 10: . Sublinear. Algorithm. Zhu Han. University of Houston. Thanks for Professor Dan Wang’s slides. 1. outline. Motivations. Inequalities and classifications . Examples. Applications. 2. 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. 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 . Block Sparse Fourier Transform. Volkan. . Cevher. Michael . Kapralov. Jonathan Scarlett. Amir . Zandieh. EPFL. 1. Discrete Fourier transform. . . root of unity . Fast Fourier Transform.
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