. A new Inexact-Restoration method for Nonlinear Programming is introduced. The iteration of the main algorithm has two phases. In Phase 1, feasibility is explicitly improved and in Phase 2 optimality is improved on a tangent approximation of the constraints. Trust regions are used for reducing the step when the trial point is not good enough. The trust region is not centered in the current point, as in many Nonlinear Programming algorithms, but in the intermediate "more feasible" point. Therefore, in this semifeasible approach, the more feasible intermediate point is considered to be essentially better than the current point. This is the first method in which intermediate-point-centered trust regions are combined with the decrease of the Lagrangian in the tangent approximation to the constraints. The merit function used in this paper is also new: it consists of a convex combination of the Lagrangian and the (non-squared) norm of the constraints. The Euclidean norm is used for simplicity but other norms for measuring infeasibility are admissible. Global convergence theorems are proved, a theoretically justified algorithm for the first phase is introduced and some numerical insight is given. Key Words: Nonlinear Programming, trust regions, GRG methods, SGRA methods, restoration methods, global convergence. 1

Inexact Restoration methods have been introduced in the last few years for solving nonlinear programming problems. These methods are related to classical restoration algorithms but also have some remarkable dierences. They generate a sequence of generally infeasible iterates with intermediate iterations that consist of inexactly restored points. The convergence theory allows one to use arbitrary algorithms for performing the restoration. This feature is appealing because it allows one to use the structure of the problem in quite opportunistic ways. Dierent Inexact Restoration algorithms are available. The most recent ones use the trust-region approach. However, unlike the algorithms based on sequential quadratic programming, the trust regions are centered not in the current point but in the inexactly restored intermediate one. Global convergence has been proved, based on merit functions of augmented Lagrangian type. In this survey we point out some applications and we relate recent advances in the theory.

In a recent paper, the authors introduced a method to estimate optical parameters of thin lms using transmission data. The associated model assumes that the lm is deposited on a completely transparent substrate. It has been observed, however, that small absorption of substrates affect in a nonnegligible way the transmitted energy. The question arises of how reliable is the estimation method to retrieve optical parameters in the presence of substrates of dierent thicknesses and absorption degrees. In this paper, transmission spectra of thin lms deposited on non-transparent substrates are generated and, as a first approximation, the method based on transparent substrates is used to estimate the optical parameters. As expected, the method is good when the absorption of the substrate is very small, but fails when one deals with less transparent substrates. To overcome this drawback, an iterative procedure is introduced, that allows one to approximate the transmittance with transparent substrate, given the transmittance with absorbent substrate. The updated method turns out to be almost as efficient in the case of absorbent substrates as it was in the case of transparent ones.

A method for linearly constrained optimization which modifies and generalizes recent box-constraint optimization algorithms is introduced. The new algorithm is based on a relaxed form of Spectral Projected Gradient iterations. Intercalated with these projected steps, internal iterations restricted to faces of the polytope are performed, which enhance the efficiency of the algorithms. Convergence proofs are given and numerical experiments are included and commented. Software supporting this paper is available through the Tango

Communicated by C. T. Leondes 1This work was supported by PRONEX-CNPq/FAPERJ Grant E-26/171.164/2003- APQ1, FAPESP Grants 03/09169-6 and 01/04597-4, and CNPq. The authors are indebted to Juliano B. Francisco and Yalcin Kaya for their careful reading of the first draft of this paper.

Nonlinear systems of equations represent often mathematical models of chemical production processes and other engineering problems. Homotopic techniques (in particular, the bounded homotopies introduced by Paloschi) are used for enhancing convergence to solutions, especially when a good initial estimate is not available. In this paper, the homotopy curve is considered as the feasible set of a mathematical programming problem, where the objective is to nd the optimal value of the homotopic parameter. Inexact restoration techniques can then be used to generate approximations in a neighborhood of the homotopy, the size of which is theoretically justied. Numerical examples are given. Key words: Nonlinear programming, homotopies, bounded homotopies, inexact restoration. 1

A new optimality condition for minimization with general constraints is introduced. Unlike the KKT conditions, this condition is satisfied by local minimizers of nonlinear programming problems, independently of constraint qualifications. The new condition implies, and is strictly stronger than, Fritz-John optimality conditions. Sufficiency for convex programming is proved.

A new optimality condition for minimization with general constraints is introduced. Unlike the KKT conditions, this condition is satisfied by local minimizers of nonlinear programming problems, independently of constraint qualifications. The new condition implies, and is strictly stronger than, Fritz-John optimality conditions. Sufficiency for convex programming is proved.