Analysis of Inexact Trust-Region SQP Algorithms
Department of Computational and Applied Mathematics
L. N. Vicente
Departamento de Matematica
Universidade de Coimbra
SIAM J. Optimization, Vol. 12 (2001), No. 2, pp. 283-302
In this paper we study the global convergence behavior of a class of
composite-step trust-region SQP methods that allow inexact problem information.
The inexact problem information can result from iterative linear systems
solves within the trust-region SQP method or from approximations of
Accuracy requirements in our trust-region SQP methods are adjusted
based on feasibility and optimality of the iterates.
In the absence of inexactness our global convergence theory is equal
to that of Dennis, El-Alem, Maciel (SIAM J. Optim., 7 (1997), pp. 177-207).
If all iterates are feasible, i.e., if all iterates
satisfy the equality constraints, then our results are related to the
known convergence analyses for trust-region methods with inexact gradient
information for unconstrained optimization.
nonlinear programming, trust-region methods,
inexact linear systems solvers, Krylov subspace methods, optimal control
AMS subject classifications
49M37, 90C06, 90C30