Optimization by Vector Space Methods
Optimization by Vector Space Methods
Penalty Methods and Augmented Lagrangians in Nonlinear Programming
5th Conference on Optimization Techniques, Part 1
Combined primal-dual and penalty function algorithms for nonlinear programming.
Combined primal-dual and penalty function algorithms for nonlinear programming.
Automatica (Journal of IFAC)
Structured sparsity via alternating direction methods
The Journal of Machine Learning Research
An inexact restoration strategy for the globalization of the sSQP method
Computational Optimization and Applications
Hi-index | 22.15 |
The purpose of this paper is to provide a survey of convergence and rate of convergence aspects of a class of recently proposed methods for constrained minimization-the, so-called, multiplier methods. The results discussed highlight the operational aspects of multiplier methods and demonstrate their significant advantages over ordinary penalty methods.