An Inexact Newton Method for Nonconvex Equality Constrained Optimization
Richard Byrd (richardcs.colorado.edu)
Abstract: We present a matrix-free line search algorithm for large-scale equality constrained optimization that allows for inexact step computations. For strictly convex problems, the method reduces to the inexact sequential quadratic programming approach proposed by Byrd et al. [SIAM J. Optim. 19(1) 351–369, 2008]. 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. Negative curvature may arise from second-order information of the problem functions, but in fact exact second derivatives are not required in the approach. 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
Category 1: Nonlinear Optimization
Category 2: Nonlinear Optimization (Constrained Nonlinear Optimization )
Category 3: Nonlinear Optimization (Systems governed by Differential Equations Optimization )
Citation: R. H. Byrd, F. E. Curtis, and J. Nocedal, “An Inexact Newton Method for Nonconvex Equality Constrained Optimization,” Mathematical Programming, 122(2): 273–299, 2010.
Entry Submitted: 08/10/2007
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