Abstract
Besides the simplex algorithm, linear programs can also be solved via interior point methods. The theoretical background of such algorithms is the classical log-barrier problem. The aim of this note is to study and generalize the barrier problem using the standard tools of Convex Analysis.
References
- D. Bayer, J. Lagarias: The nonlinear geometry of linear programming: affine and projective scaling trajectories, Trans. Amer. Math. Soc. 314 (1989) 499–525.https://doi.org/10.2307/2001396Google Scholar
- D. Bayer, J. Lagarias: The nonlinear geometry of linear programming: Legendre transform coordinates and central trajectories, Trans. Amer. Math. Soc. 314 (1989) 527–581.https://doi.org/10.2307/2001397Google Scholar
- F. Bernstein, G. Doetsch: Zur Theorie der konvexen Funktionen, Math. Ann. 76/4 (1915) 514–526.https://doi.org/10.1007/BF01458222Google Scholar
- J. M. Borwein, A. S. Lewis: Convex Analysis and Nonlinear Optimization – Theory and Examples, CMS Books in Mathematics, vol. 3, 2nd ed., Springer, New York (2006).https://doi.org/10.1007/978-0-387-31256-9Google Scholar
- G. B. Dantzig: Application of the simplex method to a transportation problem, in: Activity Analysis of Production and Allocation, Cowles Commission Monograph No. 13 (1951) 359–373.Google Scholar
- G. B. Dantzig: Linear Programming and Extensions, Princeton University Press, Princeton (1963).https://doi.org/10.1515/9781400884179Google Scholar
- A. V. Fiacco, G. P. McCormick: Nonlinear Programming: Sequential Unconstrained Minimization Techniques, John Wiley and Sons, New York (1968).Google Scholar
- P. Huard: Resolution of mathematical programming with nonlinear constraints by the method of centers, in: it Nonlinear Programming, J. Abadie (ed.), North-Holland, Amsterdam (1967) 209–219.Google Scholar
- N. Karmarkar: A new polynomial time algorithm for linear programming, Combinatorica 4 (1984) 373–395.https://doi.org/10.1007/BF02579150Google Scholar
- L. G. Khachiyan: A polynomial algorithm in linear programming, Dokl. Akad. Nauk SSSR 244/5 (1979) 1093–1096.Google Scholar
- V. Klee, G. J. Minty: How good is the simplex algorithm?, in: Inequalities III, Proceedings of the Third Symposium on Inequalities held at the University of California, Los Angeles 1969, Academic Press, New York (1972) 159–175.Google Scholar
- N. Megiddo: Pathways to the optimal set in linear programming, in: Progress in Mathematical Programming, Springer, New York (1989) 131–158.https://doi.org/10.1007/978-1-4613-9617-8_8Google Scholar
- R. Rockafellar: Convex Analysis, Princeton University Press, Princeton (1970).https://doi.org/10.1515/9781400873173Google Scholar
- C. Roos, T. Terlaky, J. P. Vial: Interior Point Methods for Linear Optimization, Springer, New York (2006).Google Scholar
- R. J. Vanderbei: Linear Programming – Foundations and Extensions, International Series in Operations Research and Management Science, vol. 196, 4th ed., Springer, New York (2014).https://doi.org/10.1007/978-1-4614-7630-6Google Scholar
- S. J. Wright: Primal-Dual Interior-Point Methods, Society for Industrial and Applied Mathematics, Philadelphia (1997).https://doi.org/10.1137/1.9781611971453Google Scholar
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Published by Heldermann Verlag, 2022. Rights now held by Banach Press LLC.
