Introduction to Stochastic Programming, 2nd Edition John R. Birge, François Louveaux
Publisher: Springer

7.1 Introduction to stochastic optimization 199. May 7, 2013 - The first part of this book presents the essential topics for an introduction to deterministic optimal control theory. 7.3 Multistage stochastic optimization 218. Nov 6, 2011 - Python is used wherever programming is involved. Chapter 4 is a completely rewritten introduction to reinforcement learning using classical concepts, with one major exception. Dec 15, 2013 - Aimed at postgraduate students, this book is also suitable for senior undergraduates, MBA students, and all those who have a general interest in business risk. In real world applications of mathematical programming, one cannot ignore the possibility that a small uncertainty in the data can make the usual optimal solution completely meaningless from a practical Stochastic optimization is a widely used and a standard approach to deal with uncertainty; for the detail of this topic one can see the books written by Birge and Louveaux [1], Kall and Mayer [2], and Prékopa [3]. 7.4 Value at risk constraints 224. The book written by Delgado et al. Nov 3, 2006 - This book is a major revision of the first edition, with seven new or heavily revised chapters. Jan 16, 2013 - (Submitted on 15 Jan 2013 (v1), last revised 11 Mar 2013 (this version, v3)). Abstract: Recently, we proposed to transform the outputs of each hidden neuron in a multi-layer perceptron We continue the work by firstly introducing a third transformation to normalize the scale of the outputs of each hidden neuron, and secondly by analyzing the connections to second order optimization methods. Note: This second edition has "grown by about 20 percent the introduction of more material on stochastic processes in evolution, a new section on genetic load theory, and a new chapter on two-locus theory. The approach is mathematical but never gets hung up on completeness, with some resort to "proof by reference".