A preconditioned variant of the Golub and Kahan (1965) bidiagonalization process recently proposed by Arioli (2013) and Arioli and Orban (2013) allows us to ...

We propose a generalization of the limited-memory Cholesky factorization of Lin and Moré (1999) to the symmetric indefinite case with special interest in sym...

Symmetric quasi-definite systems may be interpreted as regularized linear least-squares problem in appropriate metrics and arise from applications such as re...

We describe the most recent evolution of our constrained and unconstrained testing environment and its accompanying SIF decoder. Code-named SIFDecode and CU...

Projected Krylov methods are full-space formulations of Krylov methods that take place in a nullspace. Provided projections into the nullspace can be compute...

Interior-point methods feature prominently among numerical methods for inequality-constrained optimization problems, and involve the need to solve a sequ...

Interior-point methods in semi-definite programming (SDP) require the solution of a sequence of linear systems which are used to derive the search directions...

OPAL is a general-purpose system for modeling and solving algorithm optimization problems. OPAL takes an algorithm as input, and as output it suggests para...

Implementations of the Simplex method differ only in very specific aspects such as the pivot rule. Similarly, most relaxation methods for mixed-integer ...

The analytic center cutting plane method and its proximal variant are well known techniques for solving convex programming problems. We propose two seq...

We consider a class of infeasible, path-following methods for convex quadratric programming. Our methods are designed to be effective for solving both nonde...

We propose an interior-point algorithm based on an elastic formulation of the \(\ell_1\)-penalty merit function for mathematical programs with complementar...

Interior-point methods in augmented form for linear and convex quadratic programming require the solution of a sequence of symmetric indefinite linear ...

Parameterizing source code for architecture-bound optimization is a common approach to high-performance programming but one that makes the programmer's task ...

We propose a modified primal-dual interior-point method for nonlinear programming that relaxes the requirement of closely following the central path and lend...

In the context of algorithmic parameter optimization, there is much room for efficient usage of computational resources. We consider the OPAL framework in wh...

A mixed interior/exterior-point method for nonlinear programming is described, that handles constraints by way of an <i>l</i>₁-penalty function. A...

We introduce the OPAL framework in which the identification of good algorithmic parameters is interpreted as a black box optimization problem whose variables...

This intentionally short tutorial is an introduction to the main features of AMPL that are relevant to nonlinear optimization model authoring. Pointers are g...

The Improved Primal Simplex algorithm IPS [8] is a dynamic constraint reduction method particularly effective on degenerate linear programs. It is able to ac...