Description

Book Synopsis

This book provides easy access to the basic principles and methods for solving constrained and unconstrained convex optimization problems. Included are sections that cover: basic methods for solving constrained and unconstrained optimization problems with differentiable objective functions; convex sets and their properties; convex functions and their properties and generalizations; and basic principles of sub-differential calculus and convex programming problems. Convex Optimization provides detailed proofs for most of the results presented in the book and also includes many figures and exercises for a better understanding of the material. Exercises are given at the end of each chapter, with solutions and hints to selected exercises given at the end of the book. Undergraduate and graduate students, researchers in different disciplines, as well as practitioners will all benefit from this accessible approach to convex optimization methods.



Table of Contents
1. Optimization Problems with Differentiable Objective Functions.
2. Convex Sets.
3. Convex Functions.
4. Generalizations of Convex Functions.
5. Sub-gradient and Sub-differential of Finite Convex Function.
6. Constrained Optimization Problems.

Convex Optimization: Introductory Course

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    A Hardback by Mikhail Moklyachuk

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      Book details

      Publisher ISTE Ltd and John Wiley & Sons Inc
      Published 12 February 2021
      ISBN-13 9781786306838
      978-1786306838
      ISBN-10 1786306832

      Description

      Book Synopsis

      This book provides easy access to the basic principles and methods for solving constrained and unconstrained convex optimization problems. Included are sections that cover: basic methods for solving constrained and unconstrained optimization problems with differentiable objective functions; convex sets and their properties; convex functions and their properties and generalizations; and basic principles of sub-differential calculus and convex programming problems. Convex Optimization provides detailed proofs for most of the results presented in the book and also includes many figures and exercises for a better understanding of the material. Exercises are given at the end of each chapter, with solutions and hints to selected exercises given at the end of the book. Undergraduate and graduate students, researchers in different disciplines, as well as practitioners will all benefit from this accessible approach to convex optimization methods.



      Table of Contents
      1. Optimization Problems with Differentiable Objective Functions.
      2. Convex Sets.
      3. Convex Functions.
      4. Generalizations of Convex Functions.
      5. Sub-gradient and Sub-differential of Finite Convex Function.
      6. Constrained Optimization Problems.

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