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110 of 10 Reviews about "
Stochastic Programming (G.1.6...)
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Date Reviewed
Applied probability and stochastic processes
Joshua V., Varadhan S., Vishnevsky V., Springer International Publishing, New York, NY, 2020. 521 pp. Type: Book (9789811559501)
Natural phenomena are very complicated, and statistical analysis, which is categorized in the stochastic conceptual framework, is an effective tool to formulate, estimate, and predicate their nature, behaviors, and functionalities. Stochastic proc...
May 16 2023
A soft computing approach to mastering paper machines
Carlsson C., Brunelli M., Mezei J. HICSS 2013 (Proceedings of the 2013 46th Hawaii International Conference on System Sciences, Grand Wailea, Maui, HI, Jan 710, 2013) 13941401, 2013. Type: Proceedings
Mastering modern industrial processes requires the use of modern approaches in planning and optimization, and their implementation by efficient decision support systems. Recently, soft computing approaches based on fuzzy logic have bee...
Oct 16 2013
A nonMarkovian random walk underlies a stochastic model of spiketimingdependent plasticity
Elliott T. Neural Computation 22(5): 11801230, 2010. Type: Article
Elliott presents a very insightful model of spiketimingdependent plasticity (STDP), with a rigorous and thorough mathematical proof. The STDP learning rule is different from conventional Hebbian learning that can simply be stated as ...
Oct 5 2010
Stochastic processes, estimation, and control
Speyer J., Society for Industrial and Applied Mathematics, Philadelphia, PA, 2008. 400 pp. Type: Book (9780898716559)
Stochastic processes, estimators, and control theory are introduced in this book, which undergraduate students should find useful. After a brief introduction to probability theory in chapter 1, chapter 2 introduces the concept of a ran...
May 19 2009
Valid inequalities and restrictions for stochastic programming problems with first order stochastic dominance constraints
Noyan N., Ruszczyński A. Mathematical Programming: Series A 114(2): 249275, 2008. Type: Article
A typical example of a stochastic constraint problem is that of modeling portfolio optimization under the assumption that the portfolio return stochastically dominates a reference return, such as an index. This paper describes ways in ...
Dec 22 2008
Stochastic optimization
Schneider J., Kirkpatrick S., SpringerVerlag New York, Inc., Secaucus, NJ, 2006. 568 pp. Type: Book (9783540345596)
There are several challenges when dealing with optimization problems. For instance, a systematic way to explore the domain of interest must be devised. In many optimization problems, the path to the global optimum is highly nonlinear. ...
Dec 12 2007
Stochastic local search: foundations and applications
Hoos H., Stützle T., Morgan Kaufmann Publishers Inc., San Francisco, CA, 2004. 658 pp. Type: Book (9781558608726), Reviews: (2 of 2)
What can be done when a problem becomes too difficult to solve? Although many problems are known to be solvable with a reasonable amount of computational resources, even if the problem is known to be nondeterministic polynomial time (N...
Oct 2 2006
Stochastic local search: foundations and applications
Hoos H., Stützle T., Morgan Kaufmann Publishers Inc., San Francisco, CA, 2004. 658 pp. Type: Book (9781558608726), Reviews: (1 of 2)
This book clearly defines and describes the multitude of stochastic local search (SLS) methods, and their similarities and differences, and analyzes the techniques so that researchers, students, and engineers can better tailor these me...
Mar 28 2005
The cross entropy method: a unified approach to combinatorial optimization, MonteCarlo simulation (Information Science and Statistics)
Rubinstein R., Kroese D., SpringerVerlag New York, Inc., Secaucus, NJ, 2004. Type: Book (9780387212401)
Rarely have I seen such a dense and straight to the point pedagogical monograph on such a modern subject. This excellent book, on the simulated crossentropy method (CEM) pioneered by one of the authors (Rubinstein), is very well writt...
Nov 23 2004
An efficient Monte Carlo method for optimal control problems with uncertainty
Cao Y., Hussaini M., Zang T. Computational Optimization and Applications 26(3): 219230, 2003. Type: Article
After an introduction, describing optimal control problems and their range of application, the authors present a general framework for optimal control with uncertain parameters, and the sensitivity derivative Monte Carlo method (MC). A...
Apr 9 2004
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