PPT-Discrete Optimization Lecture 4 – Part 2

Author : faustina-dinatale | Published Date : 2018-02-02

M Pawan Kumar pawankumarecpfr Slides available online http mpawankumarinfo Operations on Matroids Truncation Deletion Contraction Duality of Deletion and Contraction

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Discrete Optimization Lecture 4 – Part 2: Transcript

M Pawan Kumar pawankumarecpfr Slides available online http mpawankumarinfo Operations on Matroids Truncation Deletion Contraction Duality of Deletion and Contraction Maximum Weight Independent Set. 1 Lecture Overview We ultimately wish to develop algorithms which are tailored to unconstrained optimization problems with nonsmooth objective functions constrained optimization problems problems with special structures In this lecture we examine non Module - 2 Lecture Notes – 1 Stationary points: Functions of Single and Two Variables Introduction In this session, stationary points of a function are defined. The necessary and sufficient i m Describing Inverse Problems. Syllabus. Lecture 01 Describing Inverse Problems. Lecture 02 Probability and Measurement Error, Part 1. Lecture 03 Probability and Measurement Error, Part 2 . Lecture 04 The L. for Geometry Processing. Justin Solomon. Princeton University. David . Bommes. RWTH Aachen University. This Morning’s Focus. Optimization.. Synonym(-. ish. ):. . Variational. methods.. This Morning’s Focus. Continuous Problems:. Backus-Gilbert Theory. and. Radon’s Problem. Syllabus. Lecture 01 Describing Inverse Problems. Lecture 02 Probability and Measurement Error, Part 1. Lecture 03 Probability and Measurement Error, Part 2 . Large-scale Structure from Motion. David . Crandall. School of Informatics and Computing. Indiana University. Andrew Owens. CSAIL. MIT. Noah. . Snavely. . and . Dan . Huttenlocher. Department of Computer Science. M. Pawan Kumar. pawan.kumar@ecp.fr. Slides available online http://. cvn.ecp.fr. /personnel/. pawan. /. Outline. Problem Formulation. Energy Function. Energy Minimization. Computing min-. marginals. . Large-scale Structure from Motion. David . Crandall. School of Informatics and Computing. Indiana University. Andrew Owens. CSAIL. MIT. Noah. . Snavely. . and . Dan . Huttenlocher. Department of Computer Science. Announcements:. HW . 4. . posted, . due Tues May 8 at 4:30pm. . No late HWs as solutions will be available immediately.. Midterm details on next page. HW . 5 will . be posted . Fri May 11. , . due . Backus-Gilbert Theory. and. Radon’s Problem. Syllabus. Lecture 01 Describing Inverse Problems. Lecture 02 Probability and Measurement Error, Part 1. Lecture 03 Probability and Measurement Error, Part 2 . Non-convex optimization. All loss-functions that are not convex: not very informative.. Global optimality: too strong. Weaker notions of optimality?. What is a saddle point?. Different kinds of critical/stationary points. Discrete Optimization Under Uncertainty Sahil singla Institute for Advanced Study and Princeton University Oct 2 nd , 2019 Example: How to Sell a Diamond? Sell One Diamond: potential buyers with values 139252. Mechanical Engineering Department . introduction. In . mathematics. . and . computer . science. ,. . an optimization problem is the . problem. . of . finding the best solution from all feasible . M. Pawan Kumar. pawan.kumar@ecp.fr. Slides available online http://. cvn.ecp.fr. /personnel/. pawan. Recap. V. a. V. b. V. c. d. a. d. b. d. c. Label . l. 0. Label . l. 1. D. : Observed data (image).

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