PPT-Algorithms & Adaptivity Gaps for stochastic probing

Author : karlyn-bohler | Published Date : 2017-03-15

Sahil singla Joint work with Anupam gupta and viswanath nagarajan 2 nd December 2015 Stochastic probing 2 Only 1 hour before shops close Orienteering Constraint

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Algorithms & Adaptivity Gaps for stochastic probing: Transcript

Sahil singla Joint work with Anupam gupta and viswanath nagarajan 2 nd December 2015 Stochastic probing 2 Only 1 hour before shops close Orienteering Constraint. . Hash Tables. CSE 680. Prof. Roger Crawfis. Motivation. Arrays provide an indirect way to access a . set. .. Many times we need an association between two sets, or a set of . keys. and associated data.. Some of the fastest known algorithms for certain tasks rely on chance. Stochastic/Randomized Algorithms. Two common variations. Monte Carlo. Las Vegas. We have already encountered some of both in this class. Part I: Multistage problems. Anupam. Gupta. Carnegie Mellon University. stochastic optimization. Question: . How to model uncertainty in the inputs?. data may not yet be available. obtaining exact data is difficult/expensive/time-consuming. Anupam. Gupta. Carnegie Mellon University. stochastic optimization. Question: . How to model uncertainty in the inputs?. data may not yet be available. obtaining exact data is difficult/expensive/time-consuming. Gaps for stochastic . probing. (Submodular . & XOS . Functions). Sahil. . singla. . Carnegie . mellon. university. Joint work with . Anupam. . gupta. . and. . viswanath. . nagarajan. 18. th. relaxations. via statistical query complexity. Based on:. V. F.. , Will Perkins, Santosh . Vempala. . . On the Complexity of Random Satisfiability Problems with Planted . Solutions.. STOC 2015. V. F.. Applications. Lecture . 6: . Optimize Finite Sum. Zhu Han. University of Houston. Thanks Dr. . Mingyi. Hong slides. 1. Outline (Chapter 10). Problem Formulation. Algorithms. The SAG and SAGA algorithm [Le Roux 12][. Sahil. Singla. . (Carnegie Mellon University). Thesis Committee. : . Manuel Blum. , . Anupam. Gupta. , . Robert D. Kleinberg. , . R. Ravi. , . and. . Jan . VondrÁk. (10. th. Nov, 2017). (Probing & Stopping-Time Algorithms). Anupam Gupta. Carnegie Mellon University. SODA . 2018, New Orleans. stochastic optimization. Question. : . How to . model and solve problems with . uncertainty in . input/actions?. data . not . yet . Stochastic . Optimization. Anupam Gupta. Carnegie Mellon University. IPCO Summer . School. Approximation . Algorithms for. Multi-Stage Stochastic Optimization. {vertex cover, . S. teiner tree, MSTs}. Sahil. Singla. . (Carnegie Mellon University). Joint . Work (. Partly) With . ANUPAM GUPTA . and . VISWANATH NAGARAJAN. (2. nd. Feb, 2018). Combinatorial Optimization. Given a . Finite . Universe. relaxations. via statistical query complexity. Based on:. V. F.. , Will Perkins, Santosh . Vempala. . . On the Complexity of Random Satisfiability Problems with Planted . Solutions.. STOC 2015. V. F.. Dr. Karen/Pinky Schultz. On Behalf of the CFPC Certification Process and Assessment Committee. FMF Montreal 2023. Faculty/Presenter Disclosure. Disclosure of Financial Support. Objectives -- At the end of this session, participants will be able to:. Sahil . singla. . Princeton .  Georgia Tech. Joint with . danny. . Segev. . (. Tel Aviv University). June 27. th. , 2021. Given a . Finite. . Universe : . Given an . Objective.

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