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A simplicial decomposition framework for large scale convex quadratic programming

Enrico Bettiol(bettiol***at***lipn.univ-paris13.fr)
Lucas Létocart(lucas.letocart***at***lipn.univ-paris13.fr)
Francesco Rinaldi(rinaldi***at***math.unipd.it)
Emiliano Traversi(emiliano.traversi***at***lipn.univ-paris13.fr)

Abstract: In this paper, we analyze in depth a simplicial decomposition like algorithmic framework for large scale convex quadratic programming. In particular, we first propose two tailored strategies for handling the master problem. Then, we describe a few techniques for speeding up the solution of the pricing problem. We report extensive numerical experiments on both real portfolio optimization and general quadratic programming problems showing the efficiency and robustness of the method when compared to Cplex.

Keywords: Simplicial Decomposition, Large Scale Optimization, Convex Quadratic Programming, Column Generation

Category 1: Nonlinear Optimization (Quadratic Programming )

Category 2: Convex and Nonsmooth Optimization (Convex Optimization )

Citation:

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Entry Submitted: 05/25/2017
Entry Accepted: 05/25/2017
Entry Last Modified: 05/25/2017

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