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.

Review

2018

Review

2018

Molecular dynamics (MD) simulations allow investigating the structural dynamics of biomolecular systems with unrivaled time and… Expand

Highly Cited

2013

Highly Cited

2013

Based on the classic augmented Lagrangian multiplier method, we propose, analyze and test an algorithm for solving a class of… Expand

Highly Cited

2013

Highly Cited

2013

The nuclear norm is widely used to induce low-rank solutions for many optimization problems with matrix variables. Recently, it… Expand

Highly Cited

2010

Highly Cited

2010

We present an alternating direction dual augmented Lagrangian method for solving semidefinite programming (SDP) problems in… Expand

Highly Cited

2010

Highly Cited

2010

We consider a Newton-CG augmented Lagrangian method for solving semidefinite programming (SDP) problems from the perspective of… Expand

Highly Cited

2009

Highly Cited

2009

This paper presents a new linear hyperspectral unmixing method of the minimum volume class, termed simplex identification via… Expand

Highly Cited

2008

Highly Cited

2008

Optimization methods that employ the classical Powell-Hestenes-Rockafellar augmented Lagrangian are useful tools for solving… Expand

Highly Cited

2007

Highly Cited

2007

Augmented Lagrangian methods with general lower-level constraints are considered in the present research. These methods are… Expand

Highly Cited

2006

Highly Cited

2006

We describe an effective solver for the discrete Oseen problem based on an augmented Lagrangian formulation of the corresponding… Expand

Highly Cited

2002

Highly Cited

2002

We give a pattern search method for nonlinearly constrained optimization that is an adaption of a bound constrained augmented… Expand