Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Modified barrier functions (theory and methods)
Mathematical Programming: Series A and B
A primal-dual infeasible-interior-point algorithm for linear programming
Mathematical Programming: Series A and B
Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Warm start of the primal-dual method applied in the cutting-plane scheme
Mathematical Programming: Series A and B
An Interior-Point Algorithm for Nonconvex Nonlinear Programming
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part II
Warm-Start Strategies in Interior-Point Methods for Linear Programming
SIAM Journal on Optimization
Warm Start and ε-Subgradients in a Cutting Plane Scheme
Computational Optimization and Applications
Reoptimization With the Primal-Dual Interior Point Method
SIAM Journal on Optimization
Interior-Point Algorithms, Penalty Methods and Equilibrium Problems
Computational Optimization and Applications
Interior-point methods for nonconvex nonlinear programming: regularization and warmstarts
Computational Optimization and Applications
Optimization and dynamical systems algorithms for finding equilibria of stochastic games
Optimization Methods & Software
Computational Optimization and Applications
Graph cut based inference with co-occurrence statistics
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
On Interior-Point Warmstarts for Linear and Combinatorial Optimization
SIAM Journal on Optimization
Using the primal-dual interior point algorithm within the branch-price-and-cut method
Computers and Operations Research
Inference Methods for CRFs with Co-occurrence Statistics
International Journal of Computer Vision
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One perceived deficiency of interior-point methods in comparison to active set methods is their inability to efficiently re-optimize by solving closely related problems after a warmstart. In this paper, we investigate the use of a primal---dual penalty approach to overcome this problem. We prove exactness and convergence and show encouraging numerical results on a set of linear and mixed integer programming problems.