QPALM: a proximal augmented lagrangian method for nonconvex quadratic programs
نویسندگان
چکیده
We propose QPALM, a nonconvex quadratic programming (QP) solver based on the proximal augmented Lagrangian method. This method solves sequence of inner subproblems which can be enforced to strongly convex and therefore admit unique solution. The resulting steps are shown equivalent inexact point iterations extended-real-valued cost function, allows for fairly simple analysis where convergence stationary at an \(R\)-linear rate is shown. QPALM algorithm iteratively using semismooth Newton directions exact linesearch. former computed efficiently in most by making use suitable factorization update routines, while latter requires zero monotone, one-dimensional, piecewise affine function. implemented open-source C code, with tailored linear algebra routines self-written package LADEL. implementation extremely robust numerical simulations, solving all Maros-Meszaros problems finding QPs Cutest test set. Furthermore, it competitive against state-of-the-art QP solvers typical arising from application domains such as portfolio optimization model predictive control. As such, strikes balance between both easy hard efficiently.
منابع مشابه
A Quadratic Approximation Method Based on Augmented Lagrangian Functions for Nonconvex Nonlinear Programming Problems
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ژورنال
عنوان ژورنال: Mathematical Programming Computation
سال: 2022
ISSN: ['1867-2957', '1867-2949']
DOI: https://doi.org/10.1007/s12532-022-00218-0