ProgramaÃ§Ã£o Linear (e rudimentos de otimizaÃ§Ã£o nÃ£o-linear)

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notas de aula – versão 64 - Jerônimo C. Pellegrini 196 BIBLIOGRAFIA [BV04] [Can64] [CJ99] [Cor+09] [Dan61] [DPV06] Stephen Boyd e Lieven Vandenberghe. Convex Optimization. Cambridge, 2004. ISBN: 978-0521833783. J. S. De Cani. “A dynamic programming algorithm for embedded Markov chains when the planning horizon is at infinity”. Em: Management Science 10 (1964), p. 716. Richard Courant e Fritz John. Introduction to Calculus and Analysis, Vol. II/1. Springer, 1999. ISBN: 978-3540665694. Thomas Cormen et al. Introduction to Algorithms. 3ª ed. MIT Press, 2009. George B. Dantzig. Quadratic Programming, A Variant of the Wolfe- Markovitz Algorithms. Rel. téc. OCR 61-2. Operations Research Center, University of California, Berkeley, 1961. Sanjoy Dasgupta, Christos Papadimitriou e Umesh Vazirani. Algorithms. McGraw-Hill, 2006. ISBN: 0073523402. [Dym07] Harry Dym. Linear Algebra in Action. AMS, 2007. ISBN: 978-0- 8218-3813-6. [Fil+07] Ricardo Shirota Filho et al. “Multilinear and Integer Programming for Markov Decision Processes with Imprecise Probabilities”. Em: Proceedings of the 5th International Symposium on Imprecise Probability: Theories and Applications. 2007. [FT91] Drew Fudenberg e Jean Tirole. Game Theory. MIT Press, 1991. ISBN: 978-0262061414. [GL00] [GNS09] [Gom58] [How60] Marco Cesar Goldbarg e Henrique Pacca L. Luna. Otimização Combinatória e Programação Linear: modelos e algoritmos. Campus, 2000. ISBN: 85-352-0541-1. Igor Griva, Stephen G. Nash e Ariela Sofer. Linear and Nonlinear optimization. SIAM, 2009. ISBN: 978-0898716610. R. E. Gomory. “Outline of an Algorithm for Integer Solution to Linear Programs”. Em: Bulletin of the American Mathematical Society 64.5 (1958). Ronald A. Howard. Dynamic Programming and Markov Processes. MIT Press, 1960. Versão Preliminar [How71] R. A. Howard. Semi-Markovian decision processes. Proc. Intern. Stat. Inst. (Ottawa, Canada, 1963). 1971.
notas de aula – versão 64 - Jerônimo C. Pellegrini BIBLIOGRAFIA 197 [IE94] [IN07] [Jew63] [Kar84] [Kuh55] [KV12] C. C. White III e H. K. El-Deib. “Markov decision processes with imprecise transition probabilities”. Em: Operations Research 42.4 (1994), pp. 739–749. Hideaki Itoh e Kiyohiko Nakamura. “Partially observable Markov decision processes with imprecise parameters”. Em: Artificial Intelligence 171.8-9 (2007), pp. 453–490. W. S. Jewell. “Markov-renewal programming I: Formulation, finite return models; Markov-renewal programming II, infinite return models, example”. Em: Operations Research 11 (1963), pp. 938– 971. Narendra Karmarkar. “A New Polynomial Time Algorithm for Linear Programming”. Em: Combinatorica 4.4 (1984), pp. 373– 395. H. W. Kuhn. “The Hungarian Method for the Assignment Problem”. Em: Naval Research in Logistic Quarterly 2 (1955), pp. 83– 97. Bernhard Korte e Jens Vygen. Combinatorial Optimization: Theory and Algorithms. Springer, 2012. ISBN: 978-3642244872. [Lay07] Steven R. Lay. Convex Sets and Their Applications. Dover, 2007. ISBN: 978-0486458038. [LD60] [Lue10] [MG06] [NM07] [NR10] A. H. Land e A. G. Doig. “An automatic method of solving discrete programming problems”. Em: Econometrica 28.3 (1960), pp. 497–520. David G. Luenberger. Linear and Nonlinear Programming. Springer, 2010. ISBN: 978-1441945044. Jiri Matousek e Bernd Gärtner. Understanding and Using Linear Programming. Springer, 2006. ISBN: 978-3540306979. John von Neumann e Oskar Morgenstern. Theory of Games and Economic Behavior. Princeton University Press, 2007. ISBN: 978- 0691130613. Peter Norvig e Stuart Russel. Artificial Intelligence: a modern approach. Pearson, 2010. ISBN: 0-13-604259-7. Versão Preliminar [NW06] [OR94] Jorge Nocedal e Stephen Wright. Numerical Optimization. Springer, 2006. ISBN: 978-0387303031. Martin J. Osborne e Ariel Rubinstein. A Course in Game Theory. MIT Press, 1994. ISBN: 978-0262650403.
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ProgramaÃ§Ã£o Linear (e rudimentos de otimizaÃ§Ã£o nÃ£o-linear)

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