PPT-Probing Algorithms for combinatorial optimization Under Uncertainty

Sahil Singla Carnegie Mellon University Joint Work Partly With ANUPAM GUPTA and VISWANATH NAGARAJAN 2 nd Feb 2018 Combinatorial Optimization Given a Finite Universe. TSP is one of the most famous combinatorial optimization CO problems and which has wide application background ACO has very good search capability for optimization problems but it still remains a computational bottleneck that the ACO algorithm cost 433 Combinatorial Optimization Oct 30 Nov 4 Lecture The Ellipsoid Algorithm Oct 30 Nov 4 Lecturer Santosh Vempala 1 The Algorithm for Linear Programs Problem 1 Given a polyhedron written as Ax 64257nd a poin Regrets and . Kidneys. Intro to Online Stochastic Optimization. Data revealed over time. Distribution . of future events is known. Under time constraints. Limits amount of . sampling/simulation. Solve these problems with two black boxes:. Combinatorial and Graph Algorithms. Welcome!. CS5234 Overview. Combinatorial & Graph Algorithms. http://. www.comp.nus.edu.sg/~cs5234/. Instructor: . Seth Gilbert. Office: . COM2-204. Office hours: . Optimization Algorithms. Welcome!. CS4234 . Overview. Optimization Algorithms. http://. www.comp.nus.edu.sg/. ~gilbert/CS4234. Instructor: . Seth Gilbert. Office: . COM2. -323. Office hours: . by appointment. US National Combustion Meeting‘17. April 25, 2017. University of Maryland. Pavan. B. . Govindaraju. Matthias . Ihme. Special thanks to . Tim Edwards, AFRL. CRECK Modeling Group in . Politecnico. Di Milano. Stender. Chapter 13 of Constraint Processing by . Rina. . Dechter. 3/25/2013. 1. Constraint Optimization. Motivation. 3/25/2013. 2. Constraint Optimization. Real-life problems often have both . hard. 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). Computer Vision. Medical Image Analysis. Graphics. Combinatorial . optimization algorithms . . Geometric, probabilistic, . information theoretic, and . physics based models. . Geometric methods, combinatorial algorithms. Applications. Lecture 5. : Sparse optimization. Zhu Han. University of Houston. Thanks Dr. . Shaohua. Qin’s efforts on slides. 1. Outline (chapter 4). Sparse optimization models. Classic solvers and omitted solvers (BSUM and ADMM). Classification of algorithms. The DIRECT algorithm. Divided rectangles. Exploration and Exploitation as bi-objective optimization. Application to High Speed Civil Transport. Global optimization issues. 10 Bat Algorithms Xin-She Yang, Nature-Inspired Optimization Algorithms, Elsevier, 2014 The bat algorithm (BA) is a bio-inspired algorithm developed by Xin-She Yang in 2010. 10.1 Echolocation of Bats 1. A . cover. of an undirected graph . is a set . of nodes such that every . edge . of G has at least one end in . .. For any matching . and any cover . , each edge in . has an end in . and the corresponding nodes in C are all distinct.. Uncertainty. Irreducible uncertainty . is inherent to a system. Epistemic uncertainty . is caused by the subjective lack of knowledge by the algorithm designer. In optimization problems, uncertainty can be represented by a vector of random variables .