On Solving Nonconvex Optimization Problems by Reducing The Duality Gap

Authors:
Hoang Tuy
Affiliations:
Institute of Mathematics, Hanoi, Vietnam 10307
Venue:
Journal of Global Optimization
Year:
2005

Citing 6
Cited 3

Global minimization by reducing the duality gap

Mathematical Programming: Series A and B
Convex analysis and variational problems

Convex analysis and variational problems
Duality bound method for the general quadratic programming problem with quadratic constraints

Journal of Optimization Theory and Applications
Lagrangian bounds in multiextremal polynomial and discrete optimization problems

Journal of Global Optimization
Convergence of duality bound method in partly convex programming

Journal of Global Optimization
Deterministic Global Optimization: Theory, Methods and (NONCONVEX OPTIMIZATION AND ITS APPLICATIONS Volume 37) (Nonconvex Optimization and Its Applications)

Deterministic Global Optimization: Theory, Methods and (NONCONVEX OPTIMIZATION AND ITS APPLICATIONS Volume 37) (Nonconvex Optimization and Its Applications)

A Response Surface Approach to Beam Orientation Optimization in Intensity-Modulated Radiation Therapy Treatment Planning

INFORMS Journal on Computing
Multiterm polyhedral relaxations for nonconvex, quadratically constrained quadratic programs

Optimization Methods & Software - GLOBAL OPTIMIZATION
Extended duality for nonlinear programming

Computational Optimization and Applications

Quantified Score

Hi-index	0.01

Visualization

Abstract

Lagrangian bounds, i.e. bounds computed by Lagrangian relaxation, have been used successfully in branch and bound bound methods for solving certain classes of nonconvex optimization problems by reducing the duality gap. We discuss this method for the class of partly linear and partly convex optimization problems and, incidentally, point out incorrect results in the recent literature on this subject.