Stable monotone variational inequalities
Mathematical Programming: Series A and B
An OnL -iteration homogeneous and self-dual linear programming algorithm
Mathematics of Operations Research
Smoothing methods for convex inequalities and linear complementarity problems
Mathematical Programming: Series A and B
A class of smoothing functions for nonlinear and mixed complementarity problems
Computational Optimization and Applications
Some Noninterior Continuation Methods for LinearComplementarity Problems
SIAM Journal on Matrix Analysis and Applications
Mathematics of Operations Research
On Homotopy-Smoothing Methods for Box-Constrained Variational Inequalities
SIAM Journal on Control and Optimization
A smoothing Newton method for general nonlinear complementarity problems
Computational Optimization and Applications - Special issue on nonsmooth and smoothing methods
Journal of Optimization Theory and Applications
A Global and Local Superlinear Continuation-Smoothing Method for P0 and R0 NCP or Monotone NCP
SIAM Journal on Optimization
On Smoothing Methods for the P0 Matrix Linear Complementarity Problem
SIAM Journal on Optimization
SIAM Journal on Optimization
A Globally and Locally Superlinearly Convergent Non--Interior-Point Algorithm for P0 LCPs
SIAM Journal on Optimization
Sub-quadratic convergence of a smoothing Newton algorithm for the P0– and monotone LCP
Mathematical Programming: Series A and B
A nonmonotone smoothing Newton algorithm for solving nonlinear complementarity problems
Optimization Methods & Software
Hi-index | 0.49 |
We present a smoothing-type algorithm for solving the linear program (LP) by making use of an augmented system of its optimality conditions. The algorithm is shown to be globally convergent without requiring any assumption. It only needs to solve one system of linear equations and to perform one line search at each iteration. In particular, if the LP has a solution (and hence it has a strictly complementary solution), then the algorithm will generate a strictly complementary solution of the LP; and if the LP is infeasible, then the algorithm will correctly detect infeasibility of the LP. To the best of our knowledge, this is the first smoothing-type algorithm for solving the LP having the above desired convergence features.