The lot-sizing polytope is a fundamental structure contained in many practical production planning problems. Here we study this polytope and identify facet-defining inequalities that cut off all fractional extreme points of its linear programming relaxation, as well as liftings from those facets. We give a polynomial-time combinatorial separation algorithm for the inequalities when capacities are … Read more
We study the mixed-integer knapsack polyhedron, that is, the convex hull of the mixed-integer set defined by an arbitrary linear inequality and the bounds on the variables. We describe facet-defining inequalities of this polyhedron that can be obtained through sequential lifting of inequalities containing a single integer variable. These inequalities strengthen and/or generalize known inequalities … Read more
As part of Performance World, we describe an automation server (PAVER: http://www.gamsworld.org/performance/paver) to help facilitate reproducible performance analysis of benchmarking data for optimization software. Although PAVER does not solve optimization problems, it automates the task of performance data analysis and visualization, taking into account various performance metrics. These include not only robustness and efficiency, but … Read more
We transform the system of nonlinear equations into a nonlinear programming problem, which is solved by null space algorithms. We do not use standard least square approach. We divide the equations into two groups. One group contains the equations that are treated as equality constraints. The square of other equations is regarded as objective function. … Read more
In the paper it is shown how to utilize the flexibility in native Java arrays for matrix computations. Suitable datastructures for symmetric and sparse matrices are introduced. A disadvantage of the native Java arrays is shown when used as two-dimensional array for dense matrix computation. Numerical results show that the efficiency is not lost using … Read more
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
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
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
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. Citation School … Read more
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