Mathematical Programming: Series A and B
A primal-dual interior point algorithm for linear programming
Progress in Mathematical Programming Interior-point and related methods
Pathways to the optimal set in linear programming
on Progress in Mathematical Programming: Interior-Point and Related Methods
A polynomial-time algorithm for a class of linear complementary problems
Mathematical Programming: Series A and B
Interior path following primal-dual algorithms. Part I: Linear programming
Mathematical Programming: Series A and B
Mathematics of Operations Research
Block sparse Cholesky algorithms on advanced uniprocessor computers
SIAM Journal on Scientific Computing
An OnL -iteration homogeneous and self-dual linear programming algorithm
Mathematics of Operations Research
Primal-dual algorithms for linear programming based on the logarithmic barrier method
Journal of Optimization Theory and Applications
On polynomiality of the Mehrotra-type predictor-corrector interior-point algorithms
Mathematical Programming: Series A and B
Multiple centrality corrections in a primal-dual method for linear programming
Computational Optimization and Applications
Interior point algorithms: theory and analysis
Interior point algorithms: theory and analysis
SIAM Journal on Optimization
A generalized homogeneous and self-dual algorithm for linear programming
Operations Research Letters
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We study, in the context of interior-point methods for linear programming, some possible advantages of postponing the choice of the penalty parameter and the steplength, which happens both when we apply Newton's method to the Karush-Kuhn-Tucker system and when we apply a predictor-corrector scheme. We show that for a Newton or a strictly predictor step the next iterate can be expressed as a linear function of the penalty parameter μ, and, in the case of a predictor-corrector step, as a quadratic function of μ. We also show that this parameterization is useful to guarantee either the non-negativity of the next iterate or the proximity to the central path. Initial computational results of these strategies are shown and compared with PCx, an implementation of Mehotra's predictor-corrector method.