Accelerating convergence of a globalized sequential quadratic programming method to critical Lagrange multipliers

This paper concerns the issue of asymptotic acceptance of the true Hessian and the full step by the sequential quadratic programming algorithm for equality-constrained optimization problems. In order to enforce global convergence, the algorithm is equipped with a standard Armijo linesearch procedure...

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Veröffentlicht in:	Computational optimization and applications Jg. 80; H. 3; S. 943 - 978
1. Verfasser:	Izmailov, A. F.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.12.2021 Springer Nature B.V
Schlagworte:	Algorithms Asymptotic methods Constraints Convergence Convex and Discrete Geometry Lagrange multiplier Management Science Mathematics Mathematics and Statistics Operations Research Operations Research/Decision Theory Optimization Penalty function Quadratic programming Statistics 65K05 Equality-constrained optimization Unit stepsize Merit function Lagrange optimality system Critical Lagrange multiplier 2-regularity extrapolation 90C30 Sequential quadratic programming 90C55 Newton-type methods Nonsmooth exact penalty function 65K10 49M05 Linesearch globalization of convergence True Hessian 49M15
ISSN:	0926-6003, 1573-2894
Online-Zugang:	Volltext
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