We analyze a trust region version of Newton's method for bound-constrained problems. Our approach relies on the geometry of the feasible set, not on the particular representation in terms of constraints. The convergence theory holds for linearly-constrained problems, and yields global and superlinear convergence without assuming neither strict complementarity nor linear independence of the active constraints. We also show that the convergence theory leads to an efficient implementation for large bound-constrained problems.

: We consider nonlinear programs with inequality constraints, and we focus on the problem of identifying those constraints which will be active at an isolated local solution. The correct identification of active constraints is important from both a theoretical and a practical point of view. Such an identification removes the combinatorial aspect of the problem and locally reduces the inequality constrained minimization problem to an equality constrained one which can be more easily dealt with. We present a new technique which identifies active constraints in a neighborhood of a solution and which requires neither complementary slackness nor uniqueness of the multipliers. As an example of application of the new technique we present a local active set Newton-type algorithm for the solution of general inequality constrained problems for which Q-quadratic convergence of the primal variables can be proved under very weak conditions. We also present extensions to variational inequalities. Ke...

Global methods for nonlinear complementarity problems formulate the problem as a system of nonsmooth nonlinear equations approach, or use continuation to trace a path defined by a smooth system of nonlinear equations. We formulate the nonlinear complementarity problem as a bound-constrained nonlinear least squares problem. Algorithms based on this formulation are applicable to general nonlinear complementarity problems, can be started from any nonnegative starting point, and each iteration only requires the solution of systems of linear equations. Convergence to a solution of the nonlinear complementarity problem is guaranteed under reasonable regularity assumptions. The converge rate is Q-linear, Q- superlinear, or Q-quadratic, depending on the tolerances used to solve the subproblems. Global Methods for Nonlinear Complementarity Problems Jorge J. Mor'e 1 Introduction The solution of economic equilibria has been an important motivation for the development of algorithms for nonlin...

The development of algorithms and software for the solution of large-scale optimization problems has been the main motivation behind the research on the identification properties of optimization algorithms. The aim of an identification result for a linearly constrained problem is to show that if the sequence generated by an optimization algorithm converges to a stationary point, then there is a nontrivial face F of the feasible set such that after a finite number of iterations, the iterates enter and remain in the face F . This paper develops the identification properties of linearly constrained optimization algorithms without any nondegeneracy or linear independence assumptions. The main result shows that the projected gradient converges to zero if and only if the iterates enter and remain in the face exposed by the negative gradient. This result generalizes results of Burke and Moré obtained for nondegenerate cases.

Abstract. An active set algorithm (ASA) for box constrained optimization is developed. The algorithm consists of a nonmonotone gradient projection step, an unconstrained optimization step, and a set of rules for branching between the two steps. Global convergence to a stationary point is established. For a nondegenerate stationary point, the algorithm eventually reduces to unconstrained optimization without restarts. Similarly, for a degenerate stationary point, where the strong secondorder sufficient optimality condition holds, the algorithm eventually reduces to unconstrained optimization without restarts. A specific implementation of the ASA is given which exploits the recently developed cyclic Barzilai–Borwein (CBB) algorithm for the gradient projection step and the recently developed conjugate gradient algorithm CG DESCENT for unconstrained optimization. Numerical experiments are presented using box constrained problems in the CUTEr and MINPACK-2 test problem libraries. Key words. nonmonotone gradient projection, box constrained optimization, active set algorithm,

A class of affine-scaling interior-point methods for bound constrained optimization problems is introduced which are locally q-superlinear or q-quadratic convergent. It is assumed that the strong...

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. Much progress has been made in constrained nonlinear optimization in the past ten years, but most large-scale problems still represent a considerable obstacle. In this survey paper we will attempt to give an overview of the current approaches, including interior and exterior methods and algorithms based upon trust regions and line searches. In addition, the importance of software, numerical linear algebra and testing will be addressed. We will try to explain why the difficulties arise, how attempts are being made to overcome them and some of the problems that still remain. Although there will be some emphasis on the LANCELOT and CUTE projects, the intention is to give a broad picture of the state-of-the-art. 1 IBM T.J. Watson Research Center, P.O.Box 218, Yorktown Heights, NY 10598, USA 2 Parallel Algorithms Team, CERFACS, 42 Ave. G. Coriolis, 31057 Toulouse Cedex, France 3 Central Computing Department, Rutherford Appleton Laboratory, Chilton, Oxfordshire, OX11 0QX, England ...

Abstract. Techniques that identify the active constraints at a solution of a nonlinear programming problem from a point near the solution can be a useful adjunct to nonlinear programming algorithms. They have the potential to improve the local convergence behavior of these algorithms, and in the best case can reduce an inequality constrained problem to an equality constrained problem with the same solution. This paper describes several techniques that do not require good Lagrange multiplier estimates for the constraints to be available a priori, but depend only on function and first derivative information. Computational tests comparing the effectiveness of these techniques on a variety of test problems are described. Many tests involve degenerate cases, in which the constraint gradients are not linearly independent and/or strict complementarity does not hold.

We present in this paper a class of trust region algorithms in which the structure of the problem is explicitly used in the very definition of the trust region itself. This development is intended to reflect the possibility that some parts of the problem may be more "trusted" than others, a commonly occurring situation in large-scale nonlinear applications. After describing the structured trust region mechanism, we prove global convergence for all algorithms in our class. We also prove that, when convex constraints are present, the correct set of such constraints active at the problem's solution is identified by these algorithms after a finite number of iterations.