Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first derivatives are available, and that the constraint gradients are sparse.

Several new interfaces have recently been developed requiring PATH to solve a mixed complementarity problem. To overcome the necessity of maintaining a different version of PATH for each interface, the code was reorganized using object-oriented design techniques. At the same time, robustness issues were considered and enhancements made to the algorithm. In this paper, we document the external interfaces to the PATH code and describe some of the new utilities using PATH. We then discuss the enhancements made and compare the results obtained from PATH 2.9 to the new version. 1 Introduction The PATH solver [12] for mixed complementarity problems (MCPs) was introduced in 1995 and has since become the standard against which new MCP solvers are compared. However, the main user group for PATH continues to be economists using the MPSGE preprocessor [36]. While developing the new PATH implementation, we had two goals: to make the solver accessible to a broad audience and to improve the effecti...

The GAMS modeling language has recently been extended to enable the formulation of Mixed Complementarity Problems (MCP). The GAMS Callable Program Library (CPLIB) is a set of Fortran subroutines developed as an extension for the GAMS I/O library and designed to provide a simple and convenient interface to the MCP defined by a GAMS model. This paper provides technical documentation for CPLIB for use by those who are developing or have developed algorithms for MCP, in order that their solvers may be made available as GAMS subsystems.

This paper describes Knitro 5.0, a C-package for nonlinear optimization that combines complementary approaches to nonlinear optimization to achieve robust performance over a wide range of application requirements. The package is designed for solving large-scale, smooth nonlinear programming problems, and it is also effective for the following special cases: unconstrained optimization, nonlinear systems of equations, least squares, and linear and quadratic programming. Various algorithmic options are available, including two interior methods and an active-set method. The package provides crossover techniques between algorithmic options as well as automatic selection of options and settings. 1

The Gamma Knife is a highly specialized treatment unit that provides an advanced stereotactic approach to the treatment of tumors, vascular malformations, and pain disorders within the head. Inside a shielded treatment unit, multiple beams of radiation are focussed into an approximately spherical volume, generating a high dose shot of radiation. The treatment planning process determines where to center the shots, how long to expose them for, and what size focussing helmets should be used, in order to cover the target with sufficient dosage without overdosing normal tissue or surrounding sensitive structures. We outline a new approach that models the dose distribution nonlinearly, and uses a smoothing approach to treat discrete problem choices. The resulting nonlinear program is not convex and several heuristic approaches are used to improve solution time and quality. The overall approach is fast and reliable; we give several results obtained from use in a clinical setting.

We outline a new approach for radiosurgery treatment planning, based on solving a series of optimization problems. We consider a specific treatment planning problem for a specialized device known as the Gamma Knife, that provides an advanced stereotactic approach to the treatment of tumors, vascular malformations, and pain disorders within the head. The sequence of optimization problems involves nonlinear and mixed integer programs whose solution is required in a given planning time (typically less than 30 minutes). This paper outlines several modeling decisions that result in more efficient and robust solution. Furthermore, it outlines a new approach for determining starting points for the nonlinear programs, based on a skeletonization of the target volume. Treatment plans are generated for real patient data that show the efficiency of the approach. 1

Constrained optimization has been extensively used to... This paper briefly reviews some methods available to solve these problems and describes a new suite of tools for working with MPEC models. Computational results demonstrating...

This paper applies splitting techniques developed for set-valued maximal monotone operators to monotone affine variational inequalities, including as a special case the classical linear complementarity problem. We give a unified presentation of several splitting algorithms for monotone operators, and then apply these results to obtain two classes of algorithms for affine variational inequalities. The second class resembles classical matrix splitting, but has a novel "underrelaxation " step, and converges under more general conditions. In particular, the convergence proofs do not require the affine operator to be symmetric. We specialize our matrix-splittinglike method to discrete-time optimal control problems formulated as extended linear-quadratic programs in the manner advocated by Rockafellar and Wets. The result is a highly parallel algorithm, which we implement and test on the Connection Machine CM--5 computer family. The affine variational inequality problem is to find a vector x...

Enquiries about copyright, reproduction and requests for

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.