Sequential Penalty Quadratic Programming Filter Methods for Nonlinear Programming

Filter approach is recently proposed by Fletcher and Leyffer in 2002 and is attached importance to. In this paper, the filter approach is used in an sequential penalty quadratic programming (S$l$QP) algorithm which is similar to that of Yuan’s. In every trial step, the step length is controlled by a trust region radius. If the … Read more

On an Extension of Condition Number Theory to Non-Conic Convex Optimization

The purpose of this paper is to extend, as much as possible, the modern theory of condition numbers for conic convex optimization: z_* := min_x {c’x | Ax-b \in C_Y, x \in C_X }, to the more general non-conic format: (GP_d): z_* := min_x {c’x | Ax-b \in C_Y, x \in P}, where P is … Read more

Linear Programming support in WSMP

The Watson Sparse Matrix Package (WSMP) is a high-performance robust direct solver for both symmetric and unsymmetric large sparse systems of linear equations. Currently, it works in serial, multi-threaded parallel, message-passing parallel, and a combination of message-passing and multi-threaded modes on IBM RS6000 with AIX and IA32 with Linux. The symmetric solver has features to … Read more

Asymptotic approximation method and its convergence on semi-infinite programming

The aim of this paper is to discuss an asymptotic approximation model and its convergence for the minimax semi-infinite programming problem. An asymptotic surrogate constraints method for the minimax semi-infinite programming problem is presented making use of two general iscreteapproximation methods. Simultaneously, we discuss the consistenceand the epi-convergence of the asymptotic approximation problem. CitationSchool of … Read more

On Compact Formulations for Integer Programs Solved by Column Generation

Column generation has become a powerful tool in solving large scale integer programs. We argue that most of the often reported compatibility issues between pricing oracle and branching rules disappear when branching decisions are based on the reduction of the variables of the oracle’s domain. This can be generalized to branching on variables of a … Read more

Optimization problems with equilibrium constraints and their numerical solution

We consider a class of optimization problems with a generalized equation among the constraints. This class covers several problem types like MPEC (Mathematical Programs with Equilibrium Constraints) and MPCC (Mathematical Programs with Complementarity Constraints). We briefly review techniques used for numerical solution of these problems: penalty methods, nonlinear programming (NLP) techniques and Implicit Programming approach … Read more

On the Representation and Characterization of Fullerene C60

An operation on trivalent graphs leads from the truncated cube to buckminsterfullerene, and C60 is the only fullerene with disjoint pentagons which can be obtained by this method. The construction and the proof emphasize maximal independent sets that contain two fifths of the vertices of trivalent graphs. In the case of C60, these sets define … Read more

An annotated bibliography of network interior point methods

This paper presents an annotated bibliography on interior point methods for solving network flow problems. We consider single and multi-commodity network flow problems, as well as preconditioners used in implementations of conjugate gradient methods for solving the normal systems of equations that arise in interior network flow algorithms. Applications in electrical engineering and miscellaneous papers … Read more

A Local Convergence Theory of a Filter Line Search Method for Nonlinear Programming

In this paper the theory of local convergence for a class of line search filter type methods for nonlinear programming is presented. The algorithm presented here is globally convergent (see Chin [4]) and the rate of convergence is two-step superlinear. The proposed algorithm solves a sequence of quadratic progrmming subproblems to obtain search directions and … Read more

Detecting Infeasibility in Infeasible-Interior-Point Methods for Optimization

We study interior-point methods for optimization problems in the case of infeasibility or unboundedness. While many such methods are designed to search for optimal solutions even when they do not exist, we show that they can be viewed as implicitly searching for well-defined optimal solutions to related problems whose optimal solutions give certificates of infeasibility … Read more