9783319181370-3319181378-Stochastic Multi-Stage Optimization: At the Crossroads between Discrete Time Stochastic Control and Stochastic Programming (Probability Theory and Stochastic Modelling, 75)

Stochastic Multi-Stage Optimization: At the Crossroads between Discrete Time Stochastic Control and Stochastic Programming (Probability Theory and Stochastic Modelling, 75)

ISBN-13: 9783319181370
ISBN-10: 3319181378
Edition: 2015
Author: Pierre Carpentier, Jean-Philippe Chancelier, Guy Cohen, Michel De Lara
Publication date: 2015
Publisher: Springer
Format: Hardcover 379 pages
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Book details

ISBN-13: 9783319181370
ISBN-10: 3319181378
Edition: 2015
Author: Pierre Carpentier, Jean-Philippe Chancelier, Guy Cohen, Michel De Lara
Publication date: 2015
Publisher: Springer
Format: Hardcover 379 pages

Summary

Stochastic Multi-Stage Optimization: At the Crossroads between Discrete Time Stochastic Control and Stochastic Programming (Probability Theory and Stochastic Modelling, 75) (ISBN-13: 9783319181370 and ISBN-10: 3319181378), written by authors Pierre Carpentier, Jean-Philippe Chancelier, Guy Cohen, Michel De Lara, was published by Springer in 2015. With an overall rating of 4.5 stars, it's a notable title among other Applied (Mathematics) books. You can easily purchase or rent Stochastic Multi-Stage Optimization: At the Crossroads between Discrete Time Stochastic Control and Stochastic Programming (Probability Theory and Stochastic Modelling, 75) (Hardcover) from BooksRun, along with many other new and used Applied books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.3.

Description

The focus of the present volume is stochastic optimization of dynamical systems in discrete time where - by concentrating on the role of information regarding optimization problems - it discusses the related discretization issues. There is a growing need to tackle uncertainty in applications of optimization. For example the massive introduction of renewable energies in power systems challenges traditional ways to manage them. This book lays out basic and advanced tools to handle and numerically solve such problems and thereby is building a bridge between Stochastic Programming and Stochastic Control. It is intended for graduates readers and scholars in optimization or stochastic control, as well as engineers with a background in applied mathematics.

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