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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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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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Exploring Interior-point Linear Programming

the simplex algorithm. For example, the U.S. Military airlift command has solved their scheduling problem using interior-point algorithms much;

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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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Power System Wide-area Stability Analysis and Control

of power system stability control. This book offers a systematic stochastic analysis of these nonlinear problems and provides comprehensive;

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Optimization Theory for Large Syste

Important text examines most significant algorithms for optimizing large systems and clarifying relations between optimization procedures;

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Mixed Integer Nonlinear Programming

Many engineering, operations, and scientific applications include a mixture of discrete and continuous decision variables and nonlinear;

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Nonlinear Stochastic Control and Filtering with Engineering-Oriented Complexities

Nonlinear Stochastic Control and Filtering with Engineering-oriented Complexities presents a series of control and filtering approaches for;

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Nonlinear Stochastic Control and Filtering With Engineering-Oriented Complexities

Nonlinear Stochastic Control and Filtering with Engineering-oriented Complexities presents a series of control and filtering approaches for;

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AMPL

all of AMPL's features for linear programming with extensive examples. Additional chapters cover network, nonlinear, piecewise-linear, and;

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Monte-carlo Methods and Stochastic Processes

basic tools for stochastic simulation and analysis of algorithm convergence. The second part describes Monte-Carlo methods for the simulation;

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Monte-Carlo Methods and Stochastic Processes

basic tools for stochastic simulation and analysis of algorithm convergence. The second part describes Monte-Carlo methods for the simulation;

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Stochastic Programming Problems With Probability And Quantile Functions

The concept of a system as an entity in its own right has emerged with increasing force in the past few decades in, for example, the areas;

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Operations Research Methodologies

manner. Building a bridge between theory and practice, it begins with coverage of fundamental models and methods such as linear, nonlinear;

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Descent Directions and Efficient Solutions in Discretely Distributed Stochastic Programs

can also be used to support other methods for solving discretely distributed stochastic programs, especially large scale linear programming;

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Economic Dynamics

underlying theory leads to effective algorithms for solving real world problems. The material makes extensive use of programming examples to;

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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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Continuous Nonlinear Optimization for Engineering Applications in GAMS Technolog

This book presents the theoretical details and computational performances of algorithms used for solving continuous nonlinear optimization;

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Continuous Nonlinear Optimization for Engineering Applications in GAMS Technology

This book presents the theoretical details and computational performances of algorithms used for solving continuous nonlinear optimization;

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Introduction to Linear Goal Programming

Provides a concise and lucid overview of the linear goal programming model, a computationally efficient algorithm for solution, duality and;

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Nonlinear Algebra In An Acorn

A simple algorithm for solving a set of nonlinear equations by matrix algebra has been discovered recently - first by transforming them;

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Nonlinear Expectations and Stochastic Calculus under Uncertainty

and, in particular, sublinear expectations. It provides a gentle coverage of the theory of nonlinear expectations and related stochastic;

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Self-Regularity

Research on interior-point methods (IPMs) has dominated the field of mathematical programming for the last two decades. Two contrasting;

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Explicit Nonlinear Model Predictive Control

systems without uncertainty and nonlinear systems with uncertainties (polyhedral description of uncertainty and stochastic description;

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Stochastic Programming

, methods and applications of stochastic programming. The text is intended for researchers, students, engineers and economists, who encounter;

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The Multilevel Fast Multipole Algorithm (MLFMA) for Solving Large-Scale Computational Electromagnetics Problems

The Multilevel Fast Multipole Algorithm (MLFMA) for Solving Large-Scale Computational Electromagnetic Problems provides a detailed and;

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Nonlinear Programming

interest in interior point methods generated by Karmarkar's Projective Scaling Algorithm has created a new demand for this book because the methods;

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