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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 Modeling and Optimization

This books covers the broad range of research in stochastic models and optimization. Applications presented include networks, financial;

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Control and Boundary Analysis

This volume comprises selected papers from the 21st Conference on System Modeling and Optimization in Sophia Antipolis, France. It covers;

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Control and Boundary Analysis

This volume comprises selected papers from the 21st Conference on System Modeling and Optimization in Sophia Antipolis, France. It covers;

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Stochastic Simulation Optimization For Discrete Event Systems

of stochastic simulation optimization for complex discrete event systems. This book will introduce two important techniques initially proposed and;

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Analytical and Stochastic Modeling Techniques and Applications

This book constitutes the refereed proceedings of the 20th International Conference on Analytical and Stochastic Modelling and Applications;

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Multistage Stochastic Optimization

Multistage stochastic optimization problems appear in many ways in finance, insurance, energy production and trading, logistics and;

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Dynamics of Large Structures and Inverse Problems

integrates the most recent ideas from research and industry in the field of stochastic dynamics and optimization in structural mechanics over 11;

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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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Reliability Modeling With Applications

to systems using stochastic processes to study their optimization problems. In this book, the authors will illustrate how these techniques;

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Stochastic Modeling And Analytics In Healthcare Delivery Systems

of healthcare system stochastic modeling, simulation, optimization and management, this is a valuable read.;

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Stochastic Analysis, Stochastic Systems, And Applications To Finance

with the analysis of stochastic dynamical systems, in terms of Gaussian processes, white noise theory, and diffusion processes. The second part;

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Recent Advances In Stochastic Modeling And Data Analysis

This volume presents the most recent applied and methodological issues in stochastic modeling and data analysis. The contributions cover;

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Reliability Modeling, Analysis And Optimization

research on system modeling and optimization in reliability and its applications.The book comprises twenty-three chapters organized into four parts;

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Portfolio Optimization with Different Information Flow

Portfolio Optimization with Different Information Flow recalls the stochastic tools and results concerning the stochastic;

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Adaptive Stochastic Optimization Techniques with Applications

optimization techniques used to solve problems with adaptive, dynamic, and stochastic features. Presenting modern advances in static and dynamic;

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Analytics and Optimization for Renewable Energy Integration

energy integration.The first part presents mathematical theories of stochastic mathematics; the second presents modeling and analytic techniques;

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Analytics and Optimization for Renewable Energy Integration

energy integration.The first part presents mathematical theories of stochastic mathematics; the second presents modeling and analytic techniques;

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Stochastic Global Optimization

, thus, shows significant promise and potential for process optimization.So far, there are no books focusing on stochastic global optimization;

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Stochastic Optimization with Simulation Based Optimization

Stochastic optimization is vital to making sound engineering and business decisions under uncertainty. While the limited capability;

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Stochastic Process Optimization using Aspen Plus (R)

. In this book, the combined use of the modular simulator Aspen (R) Plus and stochastic optimization methods, codified in MATLAB, is presented. Some;

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Stochastic Process Optimization using Aspen Plus (R)

combined use of the modular simulator Aspen (R) Plus and stochastic optimization methods, codified in MATLAB, is presented. Some basic concepts;

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Stochastic Volatility Modeling

practicalities of modeling local volatility, stochastic volatility, local-stochastic volatility, and multi-asset stochastic volatility. In the course;

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Advanced Optimization for Process Systems Engineering

-integer and generalized disjunctive programming), optimization under uncertainty (stochastic programming and flexibility analysis), and;

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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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Optimization Methods and Applications: In Honor of Ivan V. Sergienko's 80th Birthday

illustrates optimization models and solution methods in discrete, non-differentiable, stochastic, and nonlinear optimization. Contributions from;

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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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