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Introduction to Unconstrained Optimization with R

This book discusses unconstrained optimization with R-a free, open-source computing environment, which works on several platforms;

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Introduction to Unconstrained Optimization with R

The book highlights methods such as the steepest descent method, Newton method, conjugate direction ;...

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Nonlinear Optimization in Electrical Engineering with Applications in MATLAB (R)

Nonlinear Optimization in Electrical Engineering with Applications in MATLAB (R) provides an introductory course on nonlinear optimization;

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Solving Optimization Problems with MATLAB (R)

This book focuses on solving optimization problems with MATLAB. Descriptions and solutions of nonlinear equations of any form are studied;

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A Derivative-free Two Level Random Search Method for Unconstrained Optimization

literature and proves to be competitive for solving many unconstrained optimization problems with different structures and complexities, with a;

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Introduction to Optimization Methods

in the investigation of large systems. This book is an introduction to non-linear methods of optimization and is suitable for undergraduate and;

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Nonlinear Conjugate Gradient Methods for Unconstrained Optimization

convergence characteristics as well as their performance in solving large-scale unconstrained optimization problems and applications. Comparisons to;

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Nonlinear Conjugate Gradient Methods for Unconstrained Optimization

convergence characteristics as well as their performance in solving large-scale unconstrained optimization problems and applications. Comparisons to;

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Nonlinear Regression

comprise a self-contained introduction to unconstrained optimization. Includes many illustrative practical examples.;

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Optimization Techniques in Computer Vision

introduced and used as examples to show how each type of problem is related to typical image processing and computer vision problems. Unconstrained;

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Nonlinear Optimization

in the field of continuous optimization. It begins with a summary of classical results on unconstrained optimization, followed by a wealth;

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Nonlinear Optimization

in the field of continuous optimization. It begins with a summary of classical results on unconstrained optimization, followed by a wealth;

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Fundamentals of Optimization Techniques with Algorithms

book details program codes and computer-aided designs in relation to real-world problems. Ten chapters cover, an introduction to optimization;

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Convex Optimization

This book provides easy access to the basic principles and methods for solving constrained and unconstrained convex optimization problems;

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Numerical Optimization

This book starts with illustrations of the ubiquitous character of optimization, and describes numerical algorithms in a tutorial way. It;

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An Introduction to Optimization

of Introduction to Optimization fills the need for accessible treatment of optimization theory and methods with an emphasis on engineering design. Basic;

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Numerical Optimization

numerical methods for finite-dimensional optimization problems. It begins with very simple ideas progressing through more complicated concepts;

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Numerical Optimization

numerical methods for finite-dimensional optimization problems. It begins with very simple ideas progressing through more complicated concepts;

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A First Course in Optimization

fundamental problems of constrained and unconstrained optimization as well as linear and convex programming. It also presents basic iterative solution;

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Convex Optimization

Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and;

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Accelerated Optimization for Machine Learning

optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration;

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Accelerated Optimization for Machine Learning

optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration;

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Bayesian Optimization with Application to Computer Experiments

the core concepts of Bayesian optimization and discusses unconstrained optimization. Chapter 4 moves on to constrained optimization, and;

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An Introduction to Continuous Optimization: Foundations and Fundamental Algorithms, Third Edition

optimization problems. Starting with a brief Introduction, the text covers fundamentals, optimality conditions, linear programming, and;

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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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Financial Risk Modelling & Portfolio Opt

as an extended introduction to R language. Financial Risk Modelling and Portfolio Optimization with R: * Demonstrates techniques in modelling;

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Parallel Random Search Algorithm

of the exact solution approaches is impossible due to large computational demands. Initially designed for unconstrained optimization, the variant;

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