Sequence independent lifting for mixed-integer programming
We show that superadditive lifting functions lead to sequence independent lifting of inequalities for general mixed-integer programming. CitationOperations Research 52, 487-490, 2004
An Inventory-Location Model: Formulation, Solution Algorithm and Computational Results
We introduce a new distribution center (DC) location model that incorporates working inventory and safety stock inventory costs at the distribution centers. In addition, the model incorporates transport costs from the suppliers to the DCs that explicitly reflect economies of scale through the use of a fixed cost term. The model is formulated as a … Read more
An Interior-Point Perspective on Sensitivity Analysis in Semidefinite Programming
We study the asymptotic behavior of the interior-point bounds arising from the work of Yildirim and Todd on sensitivity analysis in semidefinite programming in comparison with the optimal partition bounds. For perturbations of the right-hand side vector and the cost matrix, we show that the interior-point bounds evaluated on the central path using the Monteiro-Zhang … Read more
A Computational Study of a Gradient-Based Log-Barrier Algorithm for a Class of Large-Scale SDPs
The authors of this paper recently introduced a transformation \cite{BuMoZh99-1} that converts a class of semidefinite programs (SDPs) into nonlinear optimization problems free of matrix-valued constraints and variables. This transformation enables the application of nonlinear optimization techniques to the solution of certain SDPs that are too large for conventional interior-point methods to handle efficiently. Based … Read more
Properties of the Log-Barrier Function on Degenerate Nonlinear Programs
We examine the sequence of local minimizers of the log-barrier function for a nonlinear program near a solution at which second-order sufficient conditions and the Mangasarian-Fromovitz constraint qualifications are satisfied, but the active constraint gradients are not necessarily linearly independent. When a strict complementarity condition is satisfied, we show uniqueness of the local minimizer of … Read more
Automatic Differentiation Tools in Optimization Software
We discuss the role of automatic differentiation tools in optimization software. We emphasize issues that are important to large-scale optimization and that have proved useful in the installation of nonlinear solvers in the NEOS Server. Our discussion centers on the computation of the gradient and Hessian matrix for partially separable functions and shows that the … Read more
Variational Analysis of Non-Lipschitz Spectral Functions
We consider spectral functions $f \circ \lambda$, where $f$ is any permutation-invariant mapping from $\Cx^n$ to $\Rl$, and $\lambda$ is the eigenvalue map from the set of $n \times n$ complex matrices to $\Cx^n$, ordering the eigenvalues lexicographically. For example, if $f$ is the function “maximum real part CitationMath. Programming 90 (2001), pp. 317-352
Variational Analysis of the Abscissa Mapping for Polynomials
The abscissa mapping on the affine variety $M_n$ of monic polynomials of degree $n$ is the mapping that takes a monic polynomial to the maximum of the real parts of its roots. This mapping plays a central role in the stability theory of matrices and dynamical systems. It is well known that the abscissa mapping … Read more
Optimizing Matrix Stability
Given an affine subspace of square matrices, we consider the problem of minimizing the spectral abscissa (the largest real part of an eigenvalue). We give an example whose optimal solution has Jordan form consisting of a single Jordan block, and we show, using nonlipschitz variational analysis, that this behaviour persists under arbitrary small perturbations to … Read more
Approximating Subdifferentials by Random Sampling of Gradients
Many interesting real functions on Euclidean space are differentiable almost everywhere. All Lipschitz functions have this property, but so, for example, does the spectral abscissa of a matrix (a non-Lipschitz function). In practice, the gradient is often easy to compute. We investigate to what extent we can approximate the Clarke subdifferential of such a function … Read more