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Optimization models / Giuseppe C. Calafiore and Laurent El Ghaoui.

By: Contributor(s): Material type: TextTextPublication details: Cambridge : Cambridge University Press, 2014.Description: xvii, 631 p. : illustrations ; 26 cmISBN:
  • 9781107050877
Subject(s): DDC classification:
  • 519.6 23 C141
Contents:
1. Introduction -- I: Linear algebra models -- 2. Vectors and functions -- 3. Matrices -- 4. Symmetric matrices -- 5. Singular value decomposition -- 6. Linear equations and least squares -- 7. Matrix algorithms -- II: Convex optimization models -- 8. Convexity -- 9. Linear, quadratic, and geometric models -- 10. Second-order cone robust models -- 11. Semidefinite models -- 12. Introduction to algorithms -- III: Applications -- 13. Learning from data -- 14. Computational finance -- 15. Control problems -- 16. Engineering design-- Index.
Summary: This book 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.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 519.6 C141 (Browse shelf(Opens below)) Available 136306
Total holds: 0

Includes index.

1. Introduction --
I: Linear algebra models --
2. Vectors and functions --
3. Matrices --
4. Symmetric matrices --
5. Singular value decomposition --
6. Linear equations and least squares --
7. Matrix algorithms --

II: Convex optimization models --
8. Convexity --
9. Linear, quadratic, and geometric models --
10. Second-order cone robust models --
11. Semidefinite models --
12. Introduction to algorithms --

III: Applications --
13. Learning from data --
14. Computational finance --
15. Control problems --
16. Engineering design--
Index.

This book 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.

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