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Fundamentals of Convex Analysis

This book is an abridged version of the two volumes Convex Analysis and Minimization Algorithms I and II (Grundlehren der mathematischen;

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Convex Analysis and Minimization Algorithms II

examples, and counter-examples, to back up the theory...To my knowledge, no other authors have given such a clear geometric account of convex;

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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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Submodular Functions and Optimization

new two chapters: "Submodular Function Minimization" and "Discrete Convex Analysis." The present 2nd edition is still a unique book on;

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Design and Analysis of Algorithms

parsing and gradient descent for convex function minimization are discussed in detail. The book's emphasis is on computational models and their;

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Lectures on Convex Optimization

literature. It contains a unified and rigorous presentation of the acceleration techniques for minimization schemes of first- and second-order. It;

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Discrete Convex Analysis

theory and applications of discrete convex analysis.;

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Proportionate-type Normalized Least Mean Square Algorithms

non-convex optimization. Milos Doroslovacki has been with the Department of Electrical and Computer Engineering at George Washington;

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Attraction in Numerical Minimization

variational analysis are extended to define and explore notions of continuity and differentiability of multiset-mappings, and prove a fixed-point;

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Nonlinear Analysis and Global Optimization

, optimization models for networks of organ transplants, network curvature measures, error analysis through energy minimization and stability problems;

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Nonlinear Programming: Theory and Algorithms (Set)

Presenting recent developments of key topics in nonlinear programming, this text looks specifically at three main areas; convex analysis;

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Constructive Real Analysis

This text introduces the methods of applied functional analysis and applied convexity. Suitable for advanced undergraduates and graduate;

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Optimality Conditions in Convex Optimization

of convex analysis-essential in developing many of the important results in this book, and not usually found in conventional texts. Unlike other;

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Logic Minimization Algorithms for VLSI Synthesis

of logic minimization for the synthesis of area and performance effective circuits clearly emerged. In 1980, Richard Newton stirred our interest by;

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Separable Optimization

and some systems of nonlinear equations are solved by separable convex unconstrained minimization problems. Some properties of the Knapsack;

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Convex and Set-Valued Analysis

-dimensional spaces A problem of linear programming More about convex sets and convex hulls Part II: Set-valued analysis Introduction to the theory;

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Convex Optimization

minimization problems, and interior-point methods. The focus of the book is on recognizing convex optimization problems and then finding the most;

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Computational Methods for Inverse Problems in Imaging

for smooth and nonsmooth convex minimization, on the use of structured (numerical) linear algebra, and on multilevel techniques. It also;

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Computational Methods for Inverse Problems in Imaging

for smooth and nonsmooth convex minimization, on the use of structured (numerical) linear algebra, and on multilevel techniques. It also;

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General Competitive Analysis

Matrices. Mathematical Appendix B: Convex and Related Sets. Mathematical Appendix C: Fixed Point Theorems and Related Combinatorial Algorithms;

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Linear and Convex Optimization

Discover the practical impacts of current methods of optimization with this approachable, one-stop resource Linear and Convex;

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Convexity and Concentration

probability theory, while Part II contains those contributions that focus primarily on problems motivated by convex geometry and geometric analysis;

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An Easy Path to Convex Analysis and Applications

taught at many universities and being used by researchers of different fields. As convex analysis is the mathematical foundation for convex;

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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

is presented, bringing together previously unrelated ideas from Banach space geometry, finite dimensional convex optimization and functional analysis;

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Nonlinear Programming: Analysis And Methods

conditions and discussions of convex programming, duality, generalized convexity, and analysis of selected nonlinear programs. The second part;

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