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A Constant-Potential Infeasible-Start Interior-Point Algorithm with Computational Experiments and Applications

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Abstract

We present a constant-potential infeasible-start interior-point (INFCP) algorithm for linear programming (LP) problems with a worst-case iteration complexity analysis as well as some computational results.The performance of the INFCP algorithm is compared to those of practical interior-point algorithms. New features of the algorithm include a heuristic method for computing a “good” starting point and a procedure for solving the augmented system arising from stochastic programming with simple recourse. We also present an application to large scale planning problems under uncertainty.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1

    Abbas Seifi

  2. Department of Combinatorics and Optimization, Faculty of Mathematics, University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1

    Levent Tunçel

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  1. Abbas Seifi
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  2. Levent Tunçel
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Seifi, A., Tunçel, L. A Constant-Potential Infeasible-Start Interior-Point Algorithm with Computational Experiments and Applications. Computational Optimization and Applications 9, 107–152 (1998). https://doi.org/10.1023/A:1018343420495

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