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A Column Generation Approach For Stochastic Optimization Problems

methods that take uncertainty into account can lead to significant reductions in cost. This book investigates the use of stochastic optimization;

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Global Optimization

This self-contained monograph presents a new stochastic approach to global optimization problems arising in a variety of disciplines;

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Designing Engineering Structures using Stochastic Optimization Methods

Among all aspects of engineering, design is the most important step in developing a new product. A systematic approach to managing design;

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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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Stochastic Optimization with Simulation Based Optimization

in stochastic dynamic risk optimization problems, where several important modification of the classical Bellman equation for stochastic dynamic problems;

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Multistage Stochastic Optimization

theory as well as numerous practical algorithms and examples for the generation and handling of scenario trees. A special emphasis is put on;

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Stochastic Process Optimization using Aspen Plus (R)

, is not an exception; thus stochastic methods are a good option to solve optimization problems for the complex process engineering models;

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Stochastic Process Optimization using Aspen Plus (R)

exception; thus stochastic methods are a good option to solve optimization problems for the complex process engineering models. In this book, the;

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Differential Evolution In Chemical Engineering

Optimization plays a key role in the design, planning and operation of chemical and related processes for several decades. Techniques for;

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Optimization for Data Analysis

of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach;

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Stochastic Global Optimization

Optimization has played a key role in the design, planning and operation of chemical and related processes, for several decades. Global;

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Stochastic Simulation Optimization

comprehensive coverage of OCBA approach for stochastic simulation optimization. Starting with an intuitive explanation of computing budget allocation;

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Stochastic Dynamics, Filtering and Optimization

solving inverse problems and a new stochastic search technique for treating a broad class of non-convex optimization problems. MATLAB (R) codes;

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Stochastic Optimization Methods: Applications in Engineering and Operations Research

differentiation formulas for probabilities and expectations. In the third edition, this book further develops stochastic optimization methods;

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Stochastic Global Optimization Methods and Applications to Chemical, Biochemical, Pharmaceutical and Environmental Processes

mainly focuses on stochastic, evolutionary, and artificial intelligence optimization algorithms with a special emphasis on their design, analysis;

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Column Generation

volume begins with A Primer in Column Generation which outlines the theory and ideas necessary to solve large-scale practical problems;

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Introduction To Stochastic Search And Optimization

A unique interdisciplinary foundation for real-world problem solving Stochastic search and optimization techniques are used in a vast;

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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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Dynamics of Large Structures and Inverse Problems

chapters. These chapters provide an update on the various tools for dealing with uncertainties, stochastic dynamics, reliability and optimization;

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Descent Directions and Efficient Solutions in Discretely Distributed Stochastic Programs

expected costs arising from the deviation between the output of a stochastic linear system and a desired stochastic target vector are minimal;

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Optimization

Choose the Correct Solution Method for Your Optimization Problem Optimization: Algorithms and Applications presents a variety;

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Stochastic Analysis and Applications

This volume attempts to exhibit current research in stochastic integration, stochastic differential equations, stochastic optimization and;

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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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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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Reliability-based Structural Design

method, reliability analysis via stochastic expansion, and optimization under uncertainty. It examines the use of stochastic expansions;

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Relative Optimization of Continuous-Time and Continuous-State Stochastic Systems

begins with an introduction to relative optimization, including a comparison with the traditional approach of dynamic programming. The text then;

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Relative Optimization of Continuous-Time and Continuous-State Stochastic Systems

begins with an introduction to relative optimization, including a comparison with the traditional approach of dynamic programming. The text then;

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