Open Access
Issue |
RAIRO-Oper. Res.
Volume 56, Number 5, September-October 2022
|
|
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Page(s) | 3643 - 3658 | |
DOI | https://doi.org/10.1051/ro/2022165 | |
Published online | 21 October 2022 |
- G.B. Dantzig, Maximization of a Linear Function of Variables Subject to Linear Inequalities. New York (1951). [Google Scholar]
- G.B. Dantzig, Linear Programming and Extensions. Princeton University (1963). [Google Scholar]
- J.V. Neumann, On a Maximization Problem. Manuscript. Institute for Advanced Studies, Princeton University, Princeton, NJ (1947). [Google Scholar]
- A. Hoffman, M. Mannos, D. Sokolowsky and N. Wiegmann, Computational experience in solving linear programs. J. Soc. Ind. Appl. Math. 1 (1953) 17–33. [CrossRef] [Google Scholar]
- N. Karmarkar, A new polynomial-time algorithm for linear programming, in Proceedings of the Sixteenth Annual ACM Symposium on Theory of Computing, ACM (1984) 302–311. [CrossRef] [Google Scholar]
- I. Dikin, Iterative solution of problems of linear and quadratic programming. Soviet Mathematics Doklady. 8 (1967) 674–675. [Google Scholar]
- E.R. Barnes, A variation on Karmarkar’s algorithm for solving linear programming problems. Math. Program. 36 (1986) 174–182. [CrossRef] [Google Scholar]
- T. Cavalier and A. Soyster, Some Computational Experience and a Modification of the Karmarkar Algorithm. Pennsylvania State Univ., College of Engineering, Department of Industrial and Management Systems Engineering (1985). [Google Scholar]
- C.C. Gonzaga, An algorithm for solving linear programming problems in o(n3l) operations, in Progress in Mathematical Programming, Springer (1989) 1–28. [Google Scholar]
- C.C. Gonzaga, Path-following methods for linear programming. SIAM Rev. 34 (1992) 167–224. [CrossRef] [MathSciNet] [Google Scholar]
- Y. Ye, An o(n3l) potential reduction algorithm for linear programming. Math. Program. 50 (1991) 239–258. [CrossRef] [Google Scholar]
- S.J. Wright, Primal-dual Interior-Point Methods. Vol. 54, SIAM (1997). [CrossRef] [Google Scholar]
- J.V. Robert, Linear Programming: Foundations and Extensions. Kluwer Academic, Boston (2001). [Google Scholar]
- J. Herskovits, Feasible direction interior-point technique for nonlinear optimization. J. Optim. Theory Appl. 99 (1998) 121–146. [CrossRef] [MathSciNet] [Google Scholar]
- A.E. Gutierrez, S.R. Mazorche, J. Herskovits and G. Chapiro, An interior point algorithm for mixed complementarity nonlinear problems. J. Optim. Theory Appl. 175 (2017) 432–449. [CrossRef] [MathSciNet] [Google Scholar]
- J.R. Roche, J. Herskovits, E. Bazán and A. Zúñiga, A feasible direction algorithm for general nonlinear semidefinite programming. Struct. Multidiscip. Optim. 55 (2017) 1261–1279. [CrossRef] [MathSciNet] [Google Scholar]
- M. Aroztegui, J. Herskovits, J.R. Roche and E. Bazá, A feasible direction interior point algorithm for nonlinear semidefinite programming. Struct. Multidiscip. Optim. 50 (2014) 1019–1035. [CrossRef] [MathSciNet] [Google Scholar]
- J. Herskovits, W.P. Freire, M. Tanaka Fo and A. Canelas, A feasible directions method for nonsmooth convex optimization. Struct. Multidiscip. Optim. 44 (2011) 363–377. [CrossRef] [MathSciNet] [Google Scholar]
- J. Herskovits, A. Leontiev, G. Dias and G. Santos, Contact shape optimization: a bilevel programming approach. Struct. Multidiscip. Optim. 20 (2000) 214–221. [CrossRef] [Google Scholar]
- R.H. Byrd, J.C. Gilbert and J. Nocedal, A trust region method based on interior point techniques for nonlinear programming. Math. Program. 89 (2000) 149–185. [CrossRef] [MathSciNet] [Google Scholar]
- A.L. Tits, J.L. Zhou and A. Simple, Quadratically Convergent Interior Point Algorithm for Linear Programming and Convex Quadratic Programming. Springer US, Boston, MA (1994) 411–427. [Google Scholar]
- N. Ploskas and N. Samaras, Linear Programming using MATLAB®. Vol. 127, Springer (2017). [CrossRef] [Google Scholar]
- Y. Saad, Iterative Methods for Sparse Linear Systems. Vol. 82, SIAM (2003). [Google Scholar]
- D.M. Gay, Electronic mail distribution of linear programming test problems. Mathematical Programming Society COAL Newsletter 13 (1985) 10–12. [Google Scholar]
- N. Maculan and M.H.C. Fampa, Otimização Linear. EdUnB, Braslia (2006). [Google Scholar]
- A.M.V. Celis, Algoritmo de ponto interior para programação linear baseado no fdipa. Master’s thesis, Universidade Federal do Rio de Janeiro (2018). [Google Scholar]
- N. Megiddo, Pathways to the optimal set in linear programming, in Progress in Mathematical Programming, Springer (1989) 131–158. [CrossRef] [Google Scholar]
- M. Kojima, S. Mizuno and A. Yoshise, A Primal-Dual Interior-Point Method for Linear Programming, Progress in Mathematical Programming: Interior-point and Related Method, Edited by N. Megiddo, Springer Verlag, New York, New York (1989) 29–47. [Google Scholar]
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