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Convex Optimization in Normed Spaces: Theory, Methods and Examples

practical tools for the numerical minimization of convex functions on Hilbert spaces. Therefore, it contains the main tools that are necessary to;

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Fuzzy Operator Theory in Mathematical Analysis

theory with applications to fuzzy metric spaces, fuzzy normed spaces, partially ordered fuzzy metric spaces, fuzzy normed algebras, and non;

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Fuzzy Operator Theory in Mathematical Analysis

theory with applications to fuzzy metric spaces, fuzzy normed spaces, partially ordered fuzzy metric spaces, fuzzy normed algebras, and non;

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Lectures on Discrete Geometry

embeddings of finite metric spaces into normed spaces. In each area, the text explains several key results and methods.;

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The Volume of Convex Bodies and Banach Space Geometry

Euclidean space and the geometry of the corresponding finite-dimensional normed spaces. The methods employ classical ideas from the theory of convex;

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Convex Optimization for Signal Processing and Communications

of convex optimization, while striking a balance between mathematical theory and applications in signal processing and communications;

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Convex Optimization for Signal Processing and Communications

of convex optimization, while striking a balance between mathematical theory and applications in signal processing and communications;

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Asymptotic Geometric Analysis

research direction. The main directions of the program included: * Asymptotic theory of convexity and normed spaces * Concentration of measure and;

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Elements of Geometry of Balls in Banach Spaces

calculus in normed spaces, approximation theory, weak topologies and reflexivity, general theory of convexity and convex functions, metric fixed;

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Introduction to the Theory of Nonlinear Optimization

This book serves as an introductory text to optimization theory in normed spaces and covers all areas of nonlinear optimization. It;

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Introduction to the Theory of Nonlinear Optimization

This book serves as an introductory text to optimization theory in normed spaces and covers all areas of nonlinear optimization. It;

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Convex Geometric Analysis

, results and, most importantly, new viewpoints, from probability theory, harmonic analysis and the geometry of finite-dimensional normed spaces;

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Convex Geometric Analysis

, results and, most importantly, new viewpoints, from probability theory, harmonic analysis and the geometry of finite-dimensional normed spaces;

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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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Lectures on Convex Optimization

expert in the field, this book includes recent advances in the algorithmic theory of convex optimization, naturally complementing the existing;

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Stability Of Functional Equations In Random Normed Spaces

basic theory of random normed spaces and related spaces; stability theory for new function equations in random normed spaces via fixed point;

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Lagrange Multiplier Approach to Variational Problems and Applications

and convex optimization problems based on the augumented Lagrangian concept and cover such topics as sensitivity analysis, convex optimization;

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Totally Convex Functions for Fixed Points Computation and Infinite Dimensional Optimization

convex optimization in infinite dimensional Banach spaces, and for problems involving, eventually, infinitely many constraints. For instance;

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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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Selected Topics in Convex Geometry

, and measure theory Can be used for graduates courses or seminars in convex geometry, geometric and convex combinatorics, and convex;

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A Mathematical View of Interior-point Methods in Convex Optimization

of the theory and emphasizing the underlying geometry, A Mathematical View of Interior-Point Methods in Convex Optimization makes the theory;

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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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Locally Convex Quasi *-Algebras and Their Representations

*-algebras, their spectral theory and a study of the structure of locally convex quasi *-algebras. Special attention is given to the case where the;

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Statistical Inference via Convex Optimization

optimization theory can be used to devise and analyze near-optimal statistical inferences. Statistical Inference via Convex Optimization is an;

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Norm Derivatives And Characterizations Of Inner Product Spaces

and generalizations of the most basic geometrical properties of triangles in normed spaces. Since the appearance of Jordan-von Neumann's;

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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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