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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Vydané v:	Optimization Ročník 68; číslo 4; s. 731 - 751
Hlavní autori:	Yousefpour, Rohollah, Jafari, Elham
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Philadelphia Taylor & Francis 03.04.2019 Taylor & Francis LLC
Predmet:	Algorithms Approximation nonlinear constraint Penalty function Quadratic programming Sequential quadratic model ε-subdifferential
ISSN:	0233-1934, 1029-4945
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