A view of algorithms for optimization without derivatives

Let the least value of a function of many variables be required. If its gradient is available, then one can tell whether search directions are downhill, and first order conditions help to identify the solution. It seems in practice, however, that the vast majority of unconstrained calculations do not employ any derivatives. A view of … Read more

Satisficing measures for analysis of risky positions

In this work we introduce a class of measures for evaluating the quality of financial positions based on their ability to achieve desired financial goals. In the spirit of Simon (1959), we call these measures satisficing measures and show that they are dual to classes of risk measures. This approach has the advantage that aspiration … Read more

Necessary optimality condition for Nonsmooth Switching Control problem

This paper is concerned with a class optimal switching nonsmoth optimal control problem is considered. Both the switching instants and the control function are to the chosen such that the cost functional is minimized.The necessary optimality conditions are derived by means of normal cone and Dubovitskii Milyutin theory. CitationTechnical report 2007-3 Submited to the journal … Read more

Separation Algorithms for 0-1 Knapsack Polytopes

Valid inequalities for 0-1 knapsack polytopes often prove useful when tackling hard 0-1 Linear Programming problems. To use such inequalities effectively, one needs separation algorithms for them, i.e., routines for detecting when they are violated. We show that the separation problems for the so-called extended cover and weight inequalities can be solved exactly in O(nb) … Read more

Improving a Formulation of the Quadratic Knapsack Problem

The Quadratic Knapsack Problem can be formulated, using an idea of Glover, as a mixed 0-1 linear program with only 2n variables. We present a simple method for strengthening that formulation, which gives good results when the profit matrix is dense and non-negative. CitationWorking Paper, Department of Management Science, Lancaster University, May 2007.ArticleDownload View PDF

On the strength of cut-based inequalities for capacitated network design polyhedra

In this paper we study capacitated network design problems, differentiating directed, bidirected and undirected link capacity models. We complement existing polyhedral results for the three variants by new classes of facet-defining valid inequalities and unified lifting results. For this, we study the restriction of the problems to a cut of the network. First, we show … Read more

An implicit trust-region method on Riemannian manifolds

We propose and analyze an “implicit” trust-region method in the general setting of Riemannian manifolds. The method is implicit in that the trust-region is defined as a superlevel set of the ratio of the actual over predicted decrease in the objective function. Since this method potentially requires the evaluation of the objective function at each … Read more

An Algorithm for the Fast Solution of Linear Complementarity Problems

This paper studies algorithms for the solution of mixed symmetric linear complementarity problems. The goal is to compute fast and approximate solutions of medium to large sized problems, such as those arising in computer game simulations and American option pricing. The paper proposes an improvement of a method described by Kocvara and Zowe that combines … Read more

Graph Modeling for Quadratic Assignment Problem Associated with the Hypercube

In the paper we consider the quadratic ssignment problem arising from channel coding in communications where one coefficient matrix is the adjacency matrix of a hypercube in a finite dimensional space. By using the geometric structure of the hypercube, we first show that there exist at least $n$ different optimal solutions to the underlying QAPs. … Read more

A polynomial predictor-corrector trust-region algorithm for linear programming

In this paper we present a scaling-invariant interior-point predictor-corrector type algorithm for linear programming (LP) whose iteration-complexity is polynomially bounded by the dimension and the logarithm of a certain condition number of the LP constraint matrix. At the predictor stage, the algorithm either takes the step along the standard affine scaling direction or a new … Read more