An SQP method for minimization of locally Lipschitz functions with nonlinear constraints

In this paper, we present a quadratic model for minimizing problems with nonconvex and nonsmooth objective and constraint functions. This method is based on sequential quadratic programming that uses an penalty function to equilibrate among the decrease of the objective function and the feasibility...

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Bibliographic Details

Published in:	Optimization Vol. 68; no. 4; pp. 731 - 751
Main Authors:	Yousefpour, Rohollah, Jafari, Elham
Format:	Journal Article
Language:	English
Published:	Philadelphia Taylor & Francis 03.04.2019 Taylor & Francis LLC
Subjects:	Algorithms Approximation nonlinear constraint Penalty function Quadratic programming Sequential quadratic model ε-subdifferential
ISSN:	0233-1934, 1029-4945
Online Access:	Get full text
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