QPAL -- A solver of convex quadratic optimization problems, using an augmented Lagrangian approach

Abstract : QPAL is a piece of software that aims at solving a convex quadratic optimization problem with linear equality and inequality constraints. The implemented algorithm uses an augmented Lagrangian approach, which relaxes the equality constraints and deals explicitely with the bound constraints on the original and slack variables. The generated quadratic functions are minimized on the activated faces by a truncated conjugate gradient algorithm, interspersed with gradient projection steps. When the optimal value is finite, convergence occurs at a linear speed that can be prescribed by the user. Matrices can be strored in dense or sparse structures; in addition, the Hessian of the quadratic objective function may have a direct of inverse $\ell$-BFGS form. QPAL is written in Fortran-2003.
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Rapport
[Technical Report] RT-0377, INRIA. 2009, pp.22
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https://hal.inria.fr/inria-00442295
Contributeur : Jean Charles Gilbert <>
Soumis le : samedi 19 décembre 2009 - 15:08:48
Dernière modification le : vendredi 16 septembre 2016 - 15:16:55
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Jean Charles Gilbert. QPAL -- A solver of convex quadratic optimization problems, using an augmented Lagrangian approach. [Technical Report] RT-0377, INRIA. 2009, pp.22. 〈inria-00442295〉

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