Negative Curvature and Second-order optimality for regularized SQP
Phillip Gill, Vyacheslav Kungurtsev, Daniel Robinson
TL;DR
This paper investigates the role of negative curvature and second-order optimality conditions in the context of regularized Sequential Quadratic Programming (SQP) methods for solving nonlinear optimization problems.
Contribution
It introduces new theoretical insights into how negative curvature can be exploited to improve second-order optimality in regularized SQP algorithms.
Findings
Established conditions under which negative curvature aids convergence
Derived new second-order optimality criteria for regularized SQP
Provided theoretical guarantees for algorithm performance
Abstract
Negative Curvature and Second-order optimality for regularized SQP
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Taxonomy
TopicsAdvanced Optimization Algorithms Research · Optimization and Variational Analysis · Advanced Control Systems Optimization