Skip to search formSkip to main content>Semantic Scholar Semantic Scholar's Logo

Search

You are currently offline. Some features of the site may not work correctly.

Semantic Scholar uses AI to extract papers important to this topic.

2013

2013

A globally convergent algorithm based on the stabilized sequential quadratic programming (sSQP) method is presented in order to… Expand

2012

2012

The proximal method is a standard regularization approach in optimization. Practical implementations of this algorithm require (i… Expand

Highly Cited

2012

Highly Cited

2012

Nonlinearly constrained optimization problems can be solved by minimizing a sequence of simpler unconstrained or linearly… Expand

Highly Cited

2011

Highly Cited

2011

An Adaptive Regularisation framework using Cubics (ARC) was proposed for unconstrained optimization and analysed in Cartis, Gould… Expand

Highly Cited

2007

Highly Cited

2007

The problem of finding good preconditioners for the numerical solution of an important class of indefinite linear systems is… Expand

Highly Cited

2006

Highly Cited

2006

Abstract.We present a primal-dual interior-point algorithm with a filter line-search method for nonlinear programming. Local and… Expand

2006

2006

Techniques that identify the active constraints at a solution of a nonlinear programming problem from a point near the solution… Expand

Highly Cited

2005

Highly Cited

2005

A new filter-trust-region algorithm for solving unconstrained nonlinear optimization problems is introduced. Based on the filter… Expand

Highly Cited

2005

Highly Cited

2005

Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with… Expand

Highly Cited

2003

Highly Cited

2003

The initial release of CUTE, a widely used testing environment for optimization software, was described by Bongartz, et al. [1995… Expand