Non-monotone trust region methods for nonlinear equality constrained optimization without a penalty function

We propose and analyze a class of penalty-function-free nonmonotone trust-region methods for nonlinear equality constrained optimization problems. The algorithmic framework yields global convergence without using a merit function and allows nonmonotonicity independently for both, the constraint viol...

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Vydané v:	Mathematical programming Ročník 95; číslo 1; s. 103 - 135
Hlavní autori:	Ulbrich, M., Ulbrich, S.
Médium:	Journal Article Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	Heidelberg Springer 01.01.2003 Springer Nature B.V
Predmet:	Algorithms Applied sciences Digital Object Identifier Exact sciences and technology Mathematical programming Methods Operational research and scientific management Operational research. Management science Optimization Quadratic programming local convergence global convergence Non linear programming Quadratic programming equality constraints Constrained optimization Penalty function large-scale optimization Sequential method Equality constraint nonmonotone trust-region methods Lagrangian function sequential quadratic programming Numerical convergence Mathematical programming Trust region method
ISSN:	0025-5610, 1436-4646
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