<--- Back to Details
First PageDocument Content
Mathematical optimization / Numerical analysis / Mathematical analysis / Operations research / Linear programming / Convex optimization / Convex analysis / Ellipsoid method / Feasible region / Convex function / Linear inequality / Candidate solution
Date: 2016-06-04 09:49:43
Mathematical optimization
Numerical analysis
Mathematical analysis
Operations research
Linear programming
Convex optimization
Convex analysis
Ellipsoid method
Feasible region
Convex function
Linear inequality
Candidate solution

CS168: The Modern Algorithmic Toolbox Lecture #18: Linear and Convex Programming, with Applications to Sparse Recovery Tim Roughgarden & Gregory Valiant∗ May 25, 2016

Add to Reading List

Source URL: theory.stanford.edu

Download Document from Source Website

File Size: 305,83 KB

Share Document on Facebook

Similar Documents

Leveraging Linear and Mixed Integer Programming for SMT Tim King1 Clark Barrett1 1 New

Leveraging Linear and Mixed Integer Programming for SMT Tim King1 Clark Barrett1 1 New

DocID: 1xVFF - View Document

Exploiting Anonymity in Approximate Linear Programming: Scaling to Large Multiagent MDPs

Exploiting Anonymity in Approximate Linear Programming: Scaling to Large Multiagent MDPs

DocID: 1xUZo - View Document

Mixed-Integer Linear Programming �LP�Branch-and-Bound Search

Mixed-Integer Linear Programming LPBranch-and-Bound Search

DocID: 1voVH - View Document

DECOMPOSITION METHODS FOR INTEGER LINEAR PROGRAMMING by  Matthew Galati

DECOMPOSITION METHODS FOR INTEGER LINEAR PROGRAMMING by Matthew Galati

DocID: 1vkrh - View Document

Approximate Linear Programming for Constrained Partially Observable Markov Decision Processes Pascal Poupart† , Aarti Malhotra† , Pei Pei† , Kee-Eung Kim§ , Bongseok Goh§ and Michael Bowling‡ †  David R. Cher

Approximate Linear Programming for Constrained Partially Observable Markov Decision Processes Pascal Poupart† , Aarti Malhotra† , Pei Pei† , Kee-Eung Kim§ , Bongseok Goh§ and Michael Bowling‡ † David R. Cher

DocID: 1vcNT - View Document