Description

Book Synopsis
Based on undergraduate teaching to students in computer science, economics and mathematics at Aarhus University, this is an elementary introduction to convex sets and convex functions with emphasis on concrete computations and examples.Starting from linear inequalities and Fourier-Motzkin elimination, the theory is developed by introducing polyhedra, the double description method and the simplex algorithm, closed convex subsets, convex functions of one and several variables ending with a chapter on convex optimization with the Karush-Kuhn-Tucker conditions, duality and an interior point algorithm. Study Guide here

Table of Contents
Introduction; Basics; The Double Description Method; Closed Convex Sets; Convex Functions of One Variable; Differentiable Functions of Several Variables; Convex Functions of Several Variables; Convex Optimization.

Undergraduate Convexity: From Fourier And Motzkin

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A Hardback by Niels Lauritzen

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    View other formats and editions of Undergraduate Convexity: From Fourier And Motzkin by Niels Lauritzen

    Publisher: World Scientific Publishing Co Pte Ltd
    Publication Date: 06/05/2013
    ISBN13: 9789814412513, 978-9814412513
    ISBN10: 9814412511
    Also in:
    Euclidean geometry

    Description

    Book Synopsis
    Based on undergraduate teaching to students in computer science, economics and mathematics at Aarhus University, this is an elementary introduction to convex sets and convex functions with emphasis on concrete computations and examples.Starting from linear inequalities and Fourier-Motzkin elimination, the theory is developed by introducing polyhedra, the double description method and the simplex algorithm, closed convex subsets, convex functions of one and several variables ending with a chapter on convex optimization with the Karush-Kuhn-Tucker conditions, duality and an interior point algorithm. Study Guide here

    Table of Contents
    Introduction; Basics; The Double Description Method; Closed Convex Sets; Convex Functions of One Variable; Differentiable Functions of Several Variables; Convex Functions of Several Variables; Convex Optimization.

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