Linearity Embedded in Nonconvex Programs

Authors:
Leo Liberti
Affiliations:
Politecnico di Milano, DEI, Milano, Italy I-20133
Venue:
Journal of Global Optimization
Year:
2005

Citing 4
Cited 6

Reduced first-level representations via the reformulation-linearization technique: results, counterexamples, and computations

Discrete Applied Mathematics
Data Structures and Algorithms

Data Structures and Algorithms
A Reduced Space Branch and Bound Algorithm for Globaloptimization

Journal of Global Optimization
Global optimization of nonconvex nonlinear programs using parallel branch and bound

Global optimization of nonconvex nonlinear programs using parallel branch and bound

A two-phase relaxation-based heuristic for the maximum feasible subsystem problem

Computers and Operations Research
Reformulation in mathematical programming: An application to quantum chemistry

Discrete Applied Mathematics
A review of recent advances in global optimization

Journal of Global Optimization
Reduced RLT representations for nonconvex polynomial programming problems

Journal of Global Optimization
Compact relaxations for polynomial programming problems

SEA'12 Proceedings of the 11th international conference on Experimental Algorithms
Theoretical filtering of RLT bound-factor constraints for solving polynomial programming problems to global optimality

Journal of Global Optimization

Quantified Score

Hi-index	0.00

Visualization

Abstract

Nonconvex programs involving bilinear terms and linear equality constraints oftenappear more nonlinear than they really are. By using an automatic symbolic reformulation wecan substitute some of the bilinear terms with linear constraints. This has a dramaticallyimproving effect on the tightness of any convex relaxation of the problem, which makesdeterministic global optimization algorithms like spatial Branch-and-Bound much more eff-cient when applied to the problem.