A new polynomial-time algorithm for linear programming
Combinatorica
Interior path following primal-dual algorithms. Part I: Linear programming
Mathematical Programming: Series A and B
Feasibility issues in a primal-dual interior-point method for linear programming
Mathematical Programming: Series A and B
A primal-dual infeasible-interior-point algorithm for linear programming
Mathematical Programming: Series A and B
An OnL -iteration homogeneous and self-dual linear programming algorithm
Mathematics of Operations Research
An infeasible-interior-point algorithm for linear complementarity problems
Mathematical Programming: Series A and B
Polynomiality of infeasible-interior-point algorithms for linear programming
Mathematical Programming: Series A and B
Mathematical Programming: Series A and B
A superquadratic infeasible-interior-point method for linear complementarity problems
Mathematical Programming: Series A and B
Interior point algorithms: theory and analysis
Interior point algorithms: theory and analysis
Mathematical Programming: Series A and B
High Order Infeasible-Interior-Point Methods for Solving Sufficient Linear Complementarity Problems
Mathematics of Operations Research
An Infeasible Path-Following Method for Monotone Complementarity Problems
SIAM Journal on Optimization
An Infeasible-Interior-Point Method for Linear Complementarity Problems
SIAM Journal on Optimization
An entire space polynomial-time algorithm for linear programming
Journal of Global Optimization
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Primal-Dual Interior-Point Methods (IPMs) have shown their power in solving large classes of optimization problems. In this paper a self-regular proximity based Infeasible Interior Point Method (IIPM) is proposed for linear optimization problems. First we mention some interesting properties of a specific self-regular proximity function, studied recently by Peng and Terlaky, and use it to define infeasible neighborhoods. These simple but interesting properties of the proximity function indicate that, when the current iterate is in a large neighborhood of the central path, large-update IIPMs emerge as the only natural choice. Then, we apply these results to design a specific self-regularity based dynamic large-update IIPM in large neighborhood. The new dynamic IIPM always takes large-updates and does not utilize any inner iteration to get centered. An $$O(n^{2}\log{\frac{n}{\epsilon}})$$ worst-case iteration bound of the algorithm is established. Finally, we report the main results of our computational experiments.