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A conjugate gradient-based algorithm for large-scale quadratic programming problem with one quadratic constraint

Akram Taati(akram.taati***at***gmail.com)
Maziar Salahi(salahi.maziar***at***gmail.com)

Abstract: In this paper, we consider the nonconvex quadratically constrained quadratic programming (QCQP) with one quadratic constraint. By employing the conjugate gradient method, an efficient algorithm is proposed to solve QCQP that exploits the sparsity of the involved matrices and solves the problem via solving a sequence of positive definite system of linear equations after identifying suitable generalized eigenvalues. Some numerical experiments are given to show the effectiveness of the proposed method and to compare it with some recent algorithms in the literature.

Keywords: QCQP, Conjugate gradient algorithm, Generalized eigenvalue problem

Category 1: Nonlinear Optimization (Quadratic Programming )

Category 2: Global Optimization

Citation:

Download: [PDF]

Entry Submitted: 07/14/2018
Entry Accepted: 07/14/2018
Entry Last Modified: 07/14/2018

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