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2010

An inexact Newton method for nonconvex equality constrained optimization

10 years 29 days ago
An inexact Newton method for nonconvex equality constrained optimization
Abstract We present a matrix-free line search algorithm for large-scale equality constrained optimization that allows for inexact step computations. For sufficiently convex problems, the method reduces to the inexact sequential quadratic programming approach proposed by Byrd, Curtis, and Nocedal [2]. For nonconvex problems, the methodology developed in this paper allows for the presence of negative curvature without requiring information about the inertia of the primal-dual iteration matrix. The complete algorithm is characterized by its emphasis on sufficient reductions in a model of an exact penalty function. We analyze the global behavior of the algorithm and present numerical results on a collection of test problems. Keywords large-scale optimization, constrained optimization, nonconvex programming, inexact linear system solvers, Krylov subspace methods CR Subject Classification 49M37, 65K05, 90C06, 90C26, 90C30
Richard H. Byrd, Frank E. Curtis, Jorge Nocedal
Added 29 Jan 2011
Updated 29 Jan 2011
Type Journal
Year 2010
Where MP
Authors Richard H. Byrd, Frank E. Curtis, Jorge Nocedal
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