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SIAMJO
2010

A Second Derivative SQP Method: Global Convergence

11 years 5 months ago
A Second Derivative SQP Method: Global Convergence
Abstract. Sequential quadratic programming (SQP) methods form a class of highly efficient algorithms for solving nonlinearly constrained optimization problems. Although second derivative information may often be calculated, there is little practical theory that justifies exact-Hessian SQP methods. In particular, the resulting quadratic programming (QP) subproblems are often nonconvex, and thus finding their global solutions may be computationally nonviable. This paper presents a second-derivative SQP method based on quadratic subproblems that are either convex, and thus may be solved efficiently, or need not be solved globally. Additionally, an explicit descent-constraint is imposed on certain QP subproblems, which “guides” the iterates through areas in which nonconvexity is a concern. Global convergence of the resulting algorithm is established. Key words. Nonlinear programming, nonlinear inequality constraints, sequential quadratic programming, ℓ1-penalty function, nonsmooth ...
Nicholas I. M. Gould, Daniel P. Robinson
Added 30 Jan 2011
Updated 30 Jan 2011
Type Journal
Year 2010
Where SIAMJO
Authors Nicholas I. M. Gould, Daniel P. Robinson
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