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Stochastic Optimization

Stochastic programming is the study of procedures for decision making under the presence of uncertainties and risks. Stochastic;

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Stochastic Programming

Brings together leading in the most important sub-fields of stochastic programming to present a rigourous overview of basic models;

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Applications of Stochastic Programming

Research on algorithms and applications of stochastic programming, the study of procedures for decision making under uncertainty over time;

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Stochastic Programming

This book is concerned with fostering theoretical issues on stochastic programming and discussing how it can solve real life problems. The;

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Adaptive Stochastic Optimization Techniques with Applications

Adaptive Stochastic Optimization Techniques with Applications provides a single, convenient source for state-of-the-art information on;

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Advances in Linear Stochastic Programming

Multi-stage Stochastic Programming (MSP) has many practical applications for solving problems whose current resolution must be made while;

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Stochastic Programming

This book shows the breadth and depth of stochastic programming applications. All the papers presented here involve optimization over the;

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Foundations and Methods of Stochastic Simulation

This graduate-level textbook covers modelling, programming and analysis of stochastic computer simulation experiments, including the;

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Foundations and Methods of Stochastic Simulation

This graduate-level textbook covers modelling, programming and analysis of stochastic computer simulation experiments, including the;

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Bilevel Programming Problems: Theory, Algorithms and Applications to Energy Networks

in particular. It describes recent applications in energy problems, such as the stochastic bilevel optimization approaches used in the natural gas;

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Applied Stochastic Control of Jump Diffusions

Here is a rigorous introduction to the most important and useful solution methods of various types of stochastic control problems for jump;

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Controlled Markov Processes and Viscosity Solutions

This book is intended as an introduction to optimal stochastic control for continuous time Markov processes and to the theory of viscosity;

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Stochastic Programming Problems With Probability And Quantile Functions

of such theory in areas of interest, or associated methodology. Of related interest Stochastic Programming Peter Kall, University of Zurich;

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BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems

generalized method for stochastic nonlinear programming based on a sampling based approach for uncertainty analysis and statistical reweighting to;

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Linear Programming

mathematical equivalence of simple recourse and chance constraints in linear stochastic programming; and provides a decomposable linear programming;

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Introduction to Stochastic Dynamic Programming

Introduction to Stochastic Dynamic Programming is een boek van Sheldon M. Ross;

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Mathematical Programming And Game Theory For Decision Making

stochastic games. Researchers, professionals and advanced graduates will find the book an essential resource for current work in mathematical;

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Stochastic Programming

This book provides an essential introduction to Stochastic Programming, especially intended for graduate students. The book begins by;

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Lectures on Stochastic Programming

Optimization problems involving stochastic models occur in almost all areas of science and engineering, such as telecommunications;

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Introduction to Linear Organization and Extensions With MATLAB

point methods for both linear and quadratic optimization as well as the inclusion of linear optimization of uncertainty i.e. stochastic;

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Decision Theory

utilities, directed networks, shortest paths, critical path analysis, scheduling and convexity Coverage of stochastic models: stochastic dynamic;

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Stochastic Linear Programming Algorithms

A computationally oriented comparison of solution algorithms for two stage and jointly chance constrained stochastic linear programming;

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Partially Observed Markov Decision Proce

processes (POMDPs). It emphasizes structural results in stochastic dynamic programming, enabling graduate students and researchers in engineering;

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Stochastic Approximation

This simple, compact toolkit for designing and analyzing stochastic approximation algorithms requires only basic literacy in probability;

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Mathematical Programming

parametric, nonlinear, discrete, stochastic, and dynamic programming. The text concludes with an appendix, in which readers unfamiliar with matrix;

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Stochastic Partial Differential Equations and Applications - VII

Stochastic Partial Differential Equations and Applications gives an overview of current state-of-the-art stochastic PDEs in several fields;

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Stability of Infinite Dimensional Stochastic Differential Equations with Applications

Stochastic differential equations in infinite dimensional spaces are motivated by the theory and analysis of stochastic processes and by;

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