An inexact Newton method for nonconvex equality constrained optimization

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. Richard H. Byrd was supported by National Science Foundation grants CCR-0219190 and CHE-0205170. Frank E. Curtis was supported by Department of Energy grant DE-FG02-87ER25047-A004. Jorge Nocedal was supported by National Science Foundation grant CCF-0514772 and by a grant from the Intel Corporation. R. H. Byrd Department of Computer Science, University of Colorado, Boulder, CO 80309, USA e-mail: [email protected] F. E. Curtis (B) Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL 60208, USA e-mail: [email protected] J. Nocedal Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USA e-mail: [email protected]