Download e-book for iPad: Applications of Stochastic Programming (MPS-SIAM Series on by Stein W. Wallace, William T. Ziemba
By Stein W. Wallace, William T. Ziemba
Learn on algorithms and purposes of stochastic programming, the learn of methods for choice making less than uncertainty through the years, has been very lively lately and merits to be extra widely recognized. this is often the 1st e-book dedicated to the complete scale of purposes of stochastic programming and in addition the 1st to supply entry to publicly to be had algorithmic platforms. The 32 contributed papers during this quantity are written by means of best stochastic programming experts and replicate the excessive point of task in recent times in study on algorithms and purposes. The ebook introduces the ability of stochastic programming to a much broader viewers and demonstrates the applying components the place this technique is improved to different modeling methods. purposes of Stochastic Programming includes components. the 1st half offers papers describing publicly to be had stochastic programming structures which are presently operational. all of the codes were greatly confirmed and built and should entice researchers and builders who intend to make versions with out broad programming and different implementation expenses. The codes are a synopsis of the simplest structures to be had, with the requirement that they be simple, able to pass, and publicly on hand. the second one a part of the publication is a various selection of program papers in components equivalent to creation, offer chain and scheduling, gaming, environmental and toxins keep an eye on, monetary modeling, telecommunications, and electrical energy. It comprises the main whole number of actual functions utilizing stochastic programming on hand within the literature. The papers convey how best researchers decide to deal with randomness while making making plans versions, with an emphasis on modeling, facts, and answer techniques. Contents Preface: half I: Stochastic Programming Codes; bankruptcy 1: Stochastic Programming desktop Implementations, Horand I. Gassmann, SteinW.Wallace, and William T. Ziemba; bankruptcy 2: The SMPS layout for Stochastic Linear courses, Horand I. Gassmann; bankruptcy three: The IBM Stochastic Programming method, Alan J. King, Stephen E.Wright, Gyana R. Parija, and Robert Entriken; bankruptcy four: SQG: software program for fixing Stochastic Programming issues of Stochastic Quasi-Gradient tools, Alexei A. Gaivoronski; bankruptcy five: Computational Grids for Stochastic Programming, Jeff Linderoth and Stephen J.Wright; bankruptcy 6: development and fixing Stochastic Linear Programming versions with SLP-IOR, Peter Kall and J?nos Mayer; bankruptcy 7: Stochastic Programming from Modeling Languages, Emmanuel Fragni?re and Jacek Gondzio; bankruptcy eight: A Stochastic Programming built-in atmosphere (SPInE), P. Valente, G. Mitra, and C. A. Poojari; bankruptcy nine: Stochastic Modelling and Optimization utilizing Stochastics™ , M. A. H. ! Dempster, J. E. Scott, and G.W. P. Thompson; bankruptcy 10: An built-in Modelling surroundings for Stochastic Programming, Horand I. Gassmann and David M. homosexual; half II: Stochastic Programming purposes; bankruptcy eleven: advent to Stochastic Programming functions Horand I. Gassmann, Sandra L. Schwartz, SteinW.Wallace, and William T. Ziemba bankruptcy 12: Fleet administration, Warren B. Powell and Huseyin Topaloglu; bankruptcy thirteen: Modeling construction making plans and Scheduling below Uncertainty, A. Alonso-Ayuso, L. F. Escudero, and M. T. Ortu?o; bankruptcy 14: A offer Chain Optimization version for the Norwegian Meat Cooperative, A. Tomasgard and E. H?eg; bankruptcy 15: soften regulate: cost Optimization through Stochastic Programming, Jitka Dupa?cov? and Pavel Popela; bankruptcy sixteen: A Stochastic Programming version for community source usage within the Presence of Multiclass call for Uncertainty, Julia L. Higle and Suvrajeet Sen; bankruptcy 17: Stochastic Optimization and Yacht Racing, A. B. Philpott; bankruptcy 18: Stochastic Approximation, Momentum, and Nash Play, H. Berglann and S. D. Fl?m; bankruptcy 19: Stochastic Optimization for Lake Eutrophication administration, Alan J. King, L?szl? Somly?dy, and Roger J
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Additional resources for Applications of Stochastic Programming (MPS-SIAM Series on Optimization)
VANDERBEI, AND S. A. ZENIOS, Robust optimization of large-scale systems, Oper. , 43 (1995), pp. 264-281.  M. C. STEINBACH, Tree-sparse convex programs, Math. Methods Oper. , 56 (2003), pp. 347-376. This page intentionally left blank Chapter 3 The IBM Stochastic Programming System Alan J. King,* Stephen E. 1 Gyana R. Parija and Introduction IBM's stochastic programming product, Optimization Solutions and Library Stochastic Extensions (OSLSE), was developed at IBM Research's Thomas J. Watson Research Center in Yorktown Heights, New York, during 1990-2002.
In practice, the SQG algorithms can be tailored to work with very imprecise estimates, for example, with s = f 0x (x, w s ), where ws is a single observation of the random vector w. Even estimates with increasing and asymptotically unbounded variance are allowed; such estimates appear sometimes in the optimization of simulation models. 5) can be relaxed even further to allow biased estimates which can result from dependent observations. 5) by the requirement of a positive scalar product between the conditional expectation of s and F0x(xs).
It is sometimes useful to be able to debug stochastic programming data input logic by examining the SMPS files or even the MPS files for the deter- 30 Chapter 3. The IBM Stochastic Programming System ministic equivalent LP. OSLSE includes functions ekks_outMatrixSMPS to write out the data in SMPS scenarios format and ekks_outMatrixMPS to write out the MPS file. Note added in proof. The product family OSL, including OSLSE, is no longer available from IBM as of March, 2004. org. We are in the process of integrating stochastic programming functionality in the COIN optimization framework, but we were not able to provide details in time for this publication.
Applications of Stochastic Programming (MPS-SIAM Series on Optimization) by Stein W. Wallace, William T. Ziemba