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The Linearization Method for Constrained Optimization

reasonable time. The lineariza- tion method is one of the fruits of this research of the last 20 years. It is closely related to Newton's method for;

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Alternating Direction Method of Multipliers for Machine Learning

of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially;

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Optimization

gradient and stochastic methods as solution techniques for unconstrained and constrained optimization problems. It discusses the conjugate gradient;

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Evolutionary Constrained Optimization

This book makes available a self-contained collection of modern research addressing the general constrained optimization problems using;

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Explanation Constrained Optimization Eco

. Morgan's Explanation of Constrained Optimization for Economists solves this problem by emphasizing explanations, both written and visual, of the;

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Global Optimization

including mathematics, operations research, engineering, and economics. The volume deals with constrained and unconstrained problems and puts a;

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Parallel Random Search Algorithm

adaptation and partial rollback procedure for constrained pseudo-Boolean optimization problems. Existing optimization algorithms are adapted for the;

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Optimization for Data Analysis

fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization;

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Frontiers in Pde-Constrained Optimization

challenging applications. Many science and engineering applications necessitate the solution of optimization problems constrained by physical laws;

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Convexity And Optimization In Rn

Convex functions Optimization problems Convex programming and duality The simplex method A detailed bibliography is included for further study;

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Real-Time PDE-Constrained Optimization

difficulties. A particularly challenging class of PDE-constrained optimization problems is characterized by the need for real-time solution, i.e;

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Linear and Nonlinear Programming

The 5th edition of this classic textbook covers the central concepts of practical optimization techniques, with an emphasis on methods that;

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Nonlinear Control Systems using MATLAB (R)

description of the feedback linearization and sliding mode control strategies Includes a simple method on how to determine the right;

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Nonlinear Control Systems using MATLAB (R)

linearization and sliding mode controllers are developed and tested using several examples. A simple method for finding the approximate sliding;

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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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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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The Feature-Driven Method for Structural Optimization

The Feature-Driven Method for Structural Optimization details a novel structural optimization method within a CAD framework;

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Engineering Optimization

applicable to the minimization of a single-variable function to those most suitable for large-scale, nonlinear constrained problems. New material;

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A New Meta-heuristic Optimization Algorithm Based on the String Theory Paradigm from Physics

for the proposed method in different cases of study. The first case, is optimization of traditional benchmark mathematical functions. The;

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New, Newer, and Newest Inequalities

Competitions. It delves into other elementary techniques but also powerful methods and generalizations for constrained optimization in the theory;

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Numerical Optimization

, concentrating on methods for both unconstrained and constrained optimization.;

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Numerical Optimization

, concentrating on methods for both unconstrained and constrained optimization.;

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Constrained Optimization and Optimal Control for Partial Differential Equations

optimization for PDE-constrained problems has undergone a dramatic increase of interest during the last four decades, a full theory for nonlinear;

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Quasilinear Control

design of linear plant or nonlinear instrumentation (LPNI) systems. The approach of QLC is based on the method of stochastic linearization, which;

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Quasilinear Control

design of linear plant or nonlinear instrumentation (LPNI) systems. The approach of QLC is based on the method of stochastic linearization, which;

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Applied Optimization

techniques for solving them. A number of engineering case studies motivate the development of efficient algorithms that involve, in some cases;

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Applied Optimization

techniques for solving them. A number of engineering case studies motivate the development of efficient algorithms that involve, in some cases;

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