Stochastic Programming: Lecture Notes in Control and Information Sciences, cartea 76
Editat de Francesco Archetti, G. Di Pillo, M. Lucertinien Limba Engleză Paperback – dec 1985
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Specificații
ISBN-13: 9783540160441
ISBN-10: 3540160442
Pagini: 296
Ilustrații: V, 287 p. 1 illus.
Dimensiuni: 170 x 244 x 16 mm
Greutate: 0.48 kg
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Lecture Notes in Control and Information Sciences
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3540160442
Pagini: 296
Ilustrații: V, 287 p. 1 illus.
Dimensiuni: 170 x 244 x 16 mm
Greutate: 0.48 kg
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Lecture Notes in Control and Information Sciences
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
Minimal time detection of parameter change in a counting process.- Simulation for passage times in non-Markovian networks of queues.- Simulation uses of the exponential distribution.- A probabilistic analysis of Monte Carlo algorithms for a class of counting problems.- An algorithm for solving linear random differential and integral equations.- Growth versus security in a risky investment model.- Queue predictors for stochastic traffic flows control.- Iterative approximations for networks of queues.- Convergence theories of distributed iterative processes: A survey.- Stochastic integer programming: The distribution problem.- The duality between expected utility and penalty in stochastic linear programming.- A feasible solution to dynamic team problems with a common past and application to decentralized dynamic routing.- Stochastic construction of (q,M) problems.- Asymptotically stable solutions to stochastic optimization problems.- On integrated chance constraints.- Algorithms based upon generalized linear programming for stochastic programs with recourse.- On the use of nested decomposition for solving nonlinear multistage stochastic programs.- Contributions to the methodology of stochastic optimization.- A method of feasible directions for solving nonsmooth stochastic programming problems.- A probabilistic analysis of the set packing problem.