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Optimization on Solution Sets of Common Fixed Point Problems

This book is devoted to a detailed study of the subgradient projection method and its variants for convex optimization problems over the;

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Optimization on Solution Sets of Common Fixed Point Problems

This book is devoted to a detailed study of the subgradient projection method and its variants for convex optimization problems over the;

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The Projected Subgradient Algorithm in Convex Optimization

it contains solutions to a number of difficult and interesting problems in the numerical optimization. The subgradient projection algorithm;

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Undergraduate Convexity

-Motzkin elimination, the theory is developed by introducing polyhedra, the double description method and the simplex algorithm, closed convex;

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Totally Convex Functions for Fixed Points Computation and Infinite Dimensional Optimization

The main purpose of this book is to present, in a unified approach, several algorithms for fixed point computation, convex feasibility and;

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Algorithms for Convex Optimization

In the last few years, Algorithms for Convex Optimization have revolutionized algorithm design, both for discrete and continuous;

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Algorithms for Convex Optimization

In the last few years, Algorithms for Convex Optimization have revolutionized algorithm design, both for discrete and continuous;

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Optimization

sequence for a saddle-point problem, and deals more with algorithms in optimization. It focuses on the multiple exchange algorithm in convex;

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Algorithms for Solving Common Fixed Point Problems

results of Chapter 6 for a dynamic string-averaging version of the proximal algorithm. In Chapters 8 subgradient projections algorithms for convex;

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Linear and Convex Optimization

, modeling, and algorithms. Convex optimization problems are covered in detail because of their many applications and the fast algorithms that have;

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Optimality Conditions in Convex Optimization

books on convex optimization, which usually discuss algorithms along with some basic theory, the sole focus of this book is on fundamental and;

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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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Convex Optimization Euclidean Distance Geometry 2e

Convex Analysis is an emerging calculus of inequalities while Convex Optimization is its application. Analysis is the domain of the;

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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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Convex Optimization & Euclidean Distance Geometry

Optimization is the science of making a best choice in the face of conflicting requirements. Any convex optimization problem has geometric;

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Convex Optimization

optimization are also provided. Lagrangian methods for numerical solutions to constrained convex programs are also explored. For enhanced algorithms;

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Selected Applications of Convex Optimization

This book focuses on the applications of convex optimization and highlights several topics, including support vector machines, parameter;

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Convex Optimization for Signal Processing and Communications

learning, especially for engineering students and professionals. With the powerful convex optimization theory and tools, this book provides you;

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Convex Optimization for Signal Processing and Communications

learning, especially for engineering students and professionals. With the powerful convex optimization theory and tools, this book provides;

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Convex Analysis for Optimization

This textbook offers graduate students a concise introduction to the classic notions of convex optimization. Written in a highly accessible;

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Convex Analysis for Optimization

This textbook offers graduate students a concise introduction to the classic notions of convex optimization. Written in a highly accessible;

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Optimization for Machine Learning

An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both;

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A First Course in Optimization

algorithms (such as gradient methods and the Newton-Raphson algorithm and its variants) and more general iterative optimization methods. This;

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Discrete Convex Analysis

Discrete Convex Analysis is a novel paradigm for discrete optimization that combines the ideas in continuous optimization (convex analysis;

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An Easy Path to Convex Analysis and Applications

optimization, having deep knowledge of convex analysis helps students and researchers apply its tools more effectively. The main goal of this book;

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Advances in Metaheuristic Algorithms for Optimal Design of Structures

Parthenogenesis Optimization algorithm. In the third edition, five new chapters are included consisting of the recently developed algorithms. These;

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Advances in Metaheuristic Algorithms for Optimal Design of Structures

Parthenogenesis Optimization algorithm. In the third edition, five new chapters are included consisting of the recently developed algorithms. These;

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