Accurate on-line support vector regression
Neural Computation
An Efficient Interior-Point Method for Convex Multicriteria Optimization Problems
Mathematics of Operations Research
Efficient Computation and Model Selection for the Support Vector Regression
Neural Computation
A Newton's method for perturbed second-order cone programs
Computational Optimization and Applications
Computational Optimization and Applications
Computational Optimization and Applications
Cutting-set methods for robust convex optimization with pessimizing oracles
Optimization Methods & Software
Processor speed control with thermal constraints
IEEE Transactions on Circuits and Systems Part I: Regular Papers
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
A decomposition-based crash-start for stochastic programming
Computational Optimization and Applications
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Reoptimization techniques for an interior point method applied to solving a sequence of linear programming problems are discussed. Conditions are given for problem perturbations that can be absorbed in merely one Newton step. The analysis is performed for both short-step and long-step feasible path-following methods. A practical procedure is then derived for an infeasible path-following method. It is applied in the context of crash start for several large-scale structured linear programs. Numerical results with OOPS, a new object-oriented parallel solver, demonstrate the efficiency of the approach. For large structured linear programs, crash start leads to about 40% reduction in the number of iterations and translates into a 25% reduction of the solution time. The crash procedure parallelizes well, and speed-ups between 3.1--3.8 on four processors are achieved.