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Optimization Models

$58.00 USD

  • Date Published: October 2014
  • availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
  • format: Adobe eBook Reader
  • isbn: 9781139990615

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  • Emphasizing practical understanding over the technicalities of specific algorithms, this elegant textbook is an accessible introduction to the field of optimization, focusing on powerful and reliable convex optimization techniques. Students and practitioners will learn how to recognize, simplify, model and solve optimization problems - and apply these principles to their own projects. A clear and self-contained introduction to linear algebra demonstrates core mathematical concepts in a way that is easy to follow, and helps students to understand their practical relevance. Requiring only a basic understanding of geometry, calculus, probability and statistics, and striking a careful balance between accessibility and rigor, it enables students to quickly understand the material, without being overwhelmed by complex mathematics. Accompanied by numerous end-of-chapter problems, an online solutions manual for instructors, and relevant examples from diverse fields including engineering, data science, economics, finance, and management, this is the perfect introduction to optimization for undergraduate and graduate students.

    • Presents a unified treatment of optimization methods and linear algebra
    • Demonstrates how abstract mathematical concepts are relevant to modern technology
    • Includes four detailed chapters demonstrating the practical application of optimization techniques to problems in machine learning, computational finance, control, and engineering design
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    Reviews & endorsements

    'In Optimization Models, Calafiore and El Ghaoui have created a beautiful and very much needed on-ramp to the world of modern mathematical optimization and its wide range of applications. They lead an undergraduate, with not much more than basic calculus behind her, from the basics of linear algebra all the way to modern optimization-based machine learning, image processing, control, and finance, to name just a few applications. Until now, these methods and topics were accessible only to graduate students in a few fields, and the few undergraduates who brave the daunting prerequisites. The book's seamless integration of mathematics and applications, and its focus on modeling practical problems and algorithmic solution methods, will be very appealing to a wide audience.' Stephen Boyd, Stanford University, California

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    Product details

    • Date Published: October 2014
    • format: Adobe eBook Reader
    • isbn: 9781139990615
    • contains: 352 b/w illus. 126 exercises
    • availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
  • Table of Contents

    1. Introduction
    Part I. Linear Algebra:
    2. Vectors
    3. Matrices
    4. Symmetric matrices
    5. Singular value decomposition
    6. Linear equations and least-squares
    7. Matrix algorithms
    Part II. Convex Optimization:
    8. Convexity
    9. Linear, quadratic and geometric models
    10. Second-order cone and robust models
    11. Semidefinite models
    12. Introduction to algorithms
    Part III. Applications:
    13. Learning from data
    14. Computational finance
    15. Control problems
    16. Engineering design.

  • Resources for

    Optimization Models

    Giuseppe C. Calafiore, Laurent El Ghaoui

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  • Authors

    Giuseppe C. Calafiore, Politecnico di Torino
    Giuseppe C. Calafiore is an Associate Professor at the Dipartimento di Automatica e Informatica, Politecnico di Torino, and a Research Fellow of the Institute of Electronics, Computer and Telecommunication Engineering, National Research Council of Italy.

    Laurent El Ghaoui, University of California, Berkeley
    Laurent El Ghaoui is a Professor in the Department of Electrical Engineering and Computer Science, the Department of Industrial Engineering and Operations Research, and the Berkeley Center for New Media, at the University of California, Berkeley.

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