A Sequential Quadratic Programming Algorithm for Nonconvex, Nonsmooth Constrained Optimization

We consider optimization problems with objective and constraint functions that may be nonconvex and nonsmooth. Problems of this type arise in important applications, many having solutions at points of nondifferentiability of the problem functions. We present a line search algorithm for situations wh...

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Bibliographic Details
Published in:SIAM journal on optimization Vol. 22; no. 2; pp. 474 - 500
Main Authors: Curtis, Frank E., Overton, Michael L.
Format: Journal Article
Language:English
Published: Philadelphia Society for Industrial and Applied Mathematics 01.01.2012
Subjects:
Algorithms
Methods
Optimization
Quadratic programming
ISSN:1052-6234, 1095-7189
Online Access:Get full text
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Summary:We consider optimization problems with objective and constraint functions that may be nonconvex and nonsmooth. Problems of this type arise in important applications, many having solutions at points of nondifferentiability of the problem functions. We present a line search algorithm for situations when the objective and constraint functions are locally Lipschitz and continuously differentiable on open dense subsets of $\mathbb{R}^{n}$. Our method is based on a sequential quadratic programming (SQP) algorithm that uses an $\ell_1$ penalty to regularize the constraints. A process of gradient sampling (GS) is employed to make the search direction computation effective in nonsmooth regions. We prove that our SQP-GS method is globally convergent to stationary points with probability one and illustrate its performance with a MATLAB implementation. [PUBLICATION ABSTRACT]
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ISSN:1052-6234
1095-7189
DOI:10.1137/090780201