STABILIZED SEQUENTIAL QUADRATIC PROGRAMMING: A SURVEY

Stabilized Sequential Quadratic Programming: a Survey

We review the motivation for, the current state-of-the-art in convergence results, and some open questions concerning the stabilized version of the sequential quadratic programming algorithm for constrained optimization. We also discuss the tools required for its local convergence analysis, globalization challenges, and extentions of the method to the more general variational problems.

Stabilized Sequential Quadratic Programming

Recently, Wright proposed a stabilized sequential quadratic programming algorithm for inequality constrained optimization. Assuming the Mangasarian-Fromovitz constraint qualification and the existence of a strictly positive multiplier (but possibly dependent constraint gradients), he proved a local quadratic convergence result. In this paper, we establish quadratic convergence in cases where bo...

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 of sSQP had been previously established under the strong second-order sufficient condition for op...

Sequential Quadratic Programming Paul

Sequential Quadratic Programming �

Introduction Since its popularization in the late s Sequential Quadratic Program ming SQP has arguably become the most successful method for solving nonlinearly constrained optimization problems As with most optimization methods SQP is not a single algorithm but rather a conceptual method from which numerous speci c algorithms have evolved Backed by a solid theoretical and computational foundat...