Collaborative Research: Further Developments in the Global Resolution of Convex Programs with Complementary Contraints
Abstract
We have developed methods for finding globally optimal solutions to various classes of nonconvex optimization problems. We have shown that any nonconvex conic quadratically constrained quadratic program can be lifted to a convex conic optimization problem. We have shown that a complementarity approach can be used to find sparse solutions to optimization problems, with promising initial theoretical and computational results. We have investigated various relaxation approaches to several classes of problems with complementarity constraints, including linear programs with complementarity constraints, support vector regression parameter selection, bi-parametric linear complementarity constrained linear programs, quadratic programs with complementarity constraints, and nonconvex quadratically constrained quadratic programs, proving various theoretical results for each of these problems as well as demonstrating the computational effectiveness of our approaches.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 31, 2014
- Accession Number
- ADA615454
Entities
People
- John E. Mitchell
- Jong-shi Pang
Organizations
- Rensselaer Polytechnic Institute