An introduction to polynomial and semi-algebraic optimization

著者

書誌事項

An introduction to polynomial and semi-algebraic optimization

Jean Bernard Lasserre

（Cambridge texts in applied mathematics）

Cambridge University Press, 2015

  • : hardback
  • : pbk

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注記

Includes bibliographical references (p. 324-336) and index

内容説明・目次

内容説明

This is the first comprehensive introduction to the powerful moment approach for solving global optimization problems (and some related problems) described by polynomials (and even semi-algebraic functions). In particular, the author explains how to use relatively recent results from real algebraic geometry to provide a systematic numerical scheme for computing the optimal value and global minimizers. Indeed, among other things, powerful positivity certificates from real algebraic geometry allow one to define an appropriate hierarchy of semidefinite (SOS) relaxations or LP relaxations whose optimal values converge to the global minimum. Several extensions to related optimization problems are also described. Graduate students, engineers and researchers entering the field can use this book to understand, experiment with and master this new approach through the simple worked examples provided.

目次

  • Preface
  • List of symbols
  • 1. Introduction and messages of the book
  • Part I. Positive Polynomials and Moment Problems: 2. Positive polynomials and moment problems
  • 3. Another look at nonnegativity
  • 4. The cone of polynomials nonnegative on K
  • Part II. Polynomial and Semi-algebraic Optimization: 5. The primal and dual points of view
  • 6. Semidefinite relaxations for polynomial optimization
  • 7. Global optimality certificates
  • 8. Exploiting sparsity or symmetry
  • 9. LP relaxations for polynomial optimization
  • 10. Minimization of rational functions
  • 11. Semidefinite relaxations for semi-algebraic optimization
  • 12. An eigenvalue problem
  • Part III. Specializations and Extensions: 13. Convexity in polynomial optimization
  • 14. Parametric optimization
  • 15. Convex underestimators of polynomials
  • 16. Inverse polynomial optimization
  • 17. Approximation of sets defined with quantifiers
  • 18. Level sets and a generalization of the Loewner-John's problem
  • Appendix A. Semidefinite programming
  • Appendix B. The GloptiPoly software
  • References
  • Index.

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