A sequential quadratic programming algorithm without a penalty function, a filter or a constraint qualification for inequality constrained optimization

This paper discusses a kind of nonlinear inequality constrained optimization problems without any constraint qualification. A new sequential quadratic programming algorithm for such problems is proposed, whose important features are as follows: (i) a new relaxation technique for the linearized const...

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Veröffentlicht in:Optimization Jg. 71; H. 6; S. 1603 - 1635
Hauptverfasser: Jian, Jinbao, Tang, Chunming, Hu, Qingjie, Han, Daolan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Philadelphia Taylor & Francis 03.06.2022
Taylor & Francis LLC
Schlagworte:
Algorithms
Constraints
finite convergence
Inequality constraints
line search
nonlinear programming
Optimization
Penalty function
Quadratic programming
sequential quadratic programming
ISSN:0233-1934, 1029-4945
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Zusammenfassung:This paper discusses a kind of nonlinear inequality constrained optimization problems without any constraint qualification. A new sequential quadratic programming algorithm for such problems is proposed, whose important features are as follows: (i) a new relaxation technique for the linearized constraints of the quadratic programming subproblem is introduced, which guarantees that the subproblem is always consistent and generates a favourable search direction; (ii) a weaker positive-definiteness assumption on the quadratic coefficient matrices is presented; (iii) a slightly new line search is adopted, where neither a penalty function nor a filter is used; (iv) an associated acceptable termination rule is introduced; (v) the finite convergence of the algorithm is proved. Furthermore, the numerical results on a collection of CUTE test problems show that the proposed algorithm is promising.
Bibliographie:ObjectType-Article-1
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ISSN:0233-1934
1029-4945
DOI:10.1080/02331934.2020.1827406