<--- Back to Details
First PageDocument Content
Statistics / Mathematical analysis / Probability / Probability distributions / Operations research / Convex optimization / Normal distribution / Linear regression / Linear programming / Sparse approximation
Date: 2011-01-09 20:39:46
Statistics
Mathematical analysis
Probability
Probability distributions
Operations research
Convex optimization
Normal distribution
Linear regression
Linear programming
Sparse approximation

Multi-Stage Dantzig Selector Ji Liu, Peter Wonka, Jieping Ye Arizona State University {ji.liu,peter.wonka,jieping.ye}@asu.edu

Add to Reading List

Source URL: peterwonka.net

Download Document from Source Website

File Size: 247,17 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