Stabilized sequential quadratic programming for optimization and a stabilized Newton-type method for variational problems

The stabilized version of the sequential quadratic programming algorithm (sSQP) had been developed in order to achieve fast convergence despite possible degeneracy of constraints of optimization problems, when the Lagrange multipliers associated to a solution are not unique. Superlinear convergence...

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Vydáno v:	Mathematical programming Ročník 125; číslo 1; s. 47 - 73
Hlavní autoři:	Fernández, Damián, Solodov, Mikhail
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer-Verlag 01.09.2010 Springer Springer Nature B.V
Témata:	Algorithms Applied sciences Calculus of variations and optimal control Calculus of Variations and Optimal Control; Optimization Combinatorics Convergence Errors Exact sciences and technology Full Length Paper Inequality Lagrange multiplier Lagrange multipliers Mathematical analysis Mathematical and Computational Physics Mathematical Methods in Physics Mathematical programming Mathematics Mathematics and Statistics Mathematics of Computing Numerical Analysis Operational research and scientific management Operational research. Management science Optimization Optimization algorithms Quadratic programming Sciences and techniques of general use Studies Theoretical 90C30 65K05 Stabilized sequential quadratic programming Variational inequality Newton methods Superlinear convergence Error bound Karush–Kuhn–Tucker system Degenerate system Karush-Kuhn-Tucker system Non linear programming Quadratic programming Constrained optimization Lagrange multiplier Sequential method Karush Kuhn Tucker method Variational calculus Optimality condition Optimality criterion Newton method Numerical convergence Mathematical programming
ISSN:	0025-5610, 1436-4646
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