Paper Info
Title
Solution Of Monotone Complementarity And General Convex Programming Problems Using A Modified Potential Reduction Interior Point Method
Abstract
We present a homogeneous algorithm equipped with a modified potential function for the monotone complementarity problem. We show that this potential function is reduced by at least a constant amount if a scaled Lipschitz condition (SLC) is satisfied. A practical algorithm based on this potential function is implemented in a software package named iOptimize. The implementation in iOptimize maintains global linear and polynomial time convergence properties, while achieving practical performance. It either successfully solves the problem, or concludes that the SLC is not satisfied. When compared with the mature software package MOSEK (barrier solver version 6.0.0.106), iOptimize solves convex quadratic programming problems, convex quadratically constrained quadratic programming problems, and general convex programming problems in fewer iterations. Moreover, several problems for which MOSEK fails are solved to optimality. We also find that iOptimize detects infeasibility more reliably than the general nonlinear solvers Ipopt (version 3.9.2) and Knitro (version 8.0).
Year
DOI
Venue
2017
10.1287/ijoc.2016.0715
INFORMS JOURNAL ON COMPUTING
Keywords
Field
DocType
quadratic programs, quadratically constrained quadratic programs, convex programs, homogeneous, algorithms, interior point methods
Second-order cone programming,Discrete mathematics,Mathematical optimization,Quadratically constrained quadratic program,Complementarity theory,Solver,Quadratic programming,Interior point method,Convex optimization,Monotone polygon,Mathematics
Journal
Volume
Issue
ISSN
29
1
1091-9856
Citations 
PageRank 
References 
1
0.37
14
Authors
2
Name
Order
Citations
PageRank
Kuo-Ling Huang1804.95
Sanjay Mehrotra252177.18