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Moments, Positive Polynomials And Their Applications.

Many important applications in global optimization, algebra, probability and statistics, applied mathematics, control theory, financial mathematics, inverse problems, etc. can be modeled as a particular instance of the Generalized Moment Problem (GMP) . This book introduces a new general methodology...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Formato: Electrónico eBook
Idioma:Inglés
Publicado: World Scientific 2009.
Temas:
Acceso en línea:Texto completo

MARC

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505 0 |a Cover13; -- Contents -- Preface -- Acknowledgments -- Part I Moments and Positive Polynomials -- 1. The Generalized Moment Problem -- 1.1 Formulations -- 1.2 Duality Theory -- 1.3 Computational Complexity -- 1.4 Summary -- 1.5 Exercises -- 1.6 Notes and Sources -- 2. Positive Polynomials -- 2.1 Sum of Squares Representations and Semi-de nite Optimization -- 2.2 Nonnegative Versus s.o.s. Polynomials -- 2.3 Representation Theorems: Univariate Case -- 2.4 Representation Theorems: Mutivariate Case -- 2.5 Polynomials Positive on a Compact Basic Semi-algebraic Set -- 2.6 Polynomials Nonnegative on Real Varieties -- 2.7 Representations with Sparsity Properties -- 2.8 Representation of Convex Polynomials -- 2.9 Summary -- 2.10 Exercises -- 2.11 Notes and Sources -- 3. Moments -- 3.1 The One-dimensional Moment Problem -- 3.2 The Multi-dimensional Moment Problem -- 3.3 The K-moment Problem -- 3.4 Moment Conditions for Bounded Density -- 3.5 Summary -- 3.6 Exercises -- 3.7 Notes and Sources -- 4. Algorithms for Moment Problems -- 4.1 The Overall Approach -- 4.2 Semide nite Relaxations -- 4.3 Extraction of Solutions -- 4.4 Linear Relaxations -- 4.5 Extensions -- 4.6 Exploiting Sparsity -- 4.7 Summary -- 4.8 Exercises -- 4.9 Notes and Sources -- 4.10 Proofs -- Part II Applications -- 5. Global Optimization over Polynomials -- 5.1 The Primal and Dual Perspectives -- 5.2 Unconstrained Polynomial Optimization -- 5.3 Constrained Polynomial Optimization: Semide nite Relaxations -- 5.4 Linear Programming Relaxations -- 5.5 Global Optimality Conditions -- 5.6 Convex Polynomial Programs -- 5.7 Discrete Optimization -- 5.8 Global Minimization of a Rational Function -- 5.9 Exploiting Symmetry -- 5.10 Summary -- 5.11 Exercises -- 5.12 Notes and Sources -- 6. Systems of Polynomial Equations -- 6.1 Introduction -- 6.2 Finding a Real Solution to Systems of Polynomial Equations -- 6.3 Finding All Complex and/or All Real Solutions: A Uni ed Treatment -- 6.4 Summary -- 6.5 Exercises -- 6.6 Notes and Sources -- 7. Applications in Probability -- 7.1 Upper Bounds on Measures with Moment Conditions -- 7.2 Measuring Basic Semi-algebraic Sets -- 7.3 Measures with Given Marginals -- 7.4 Summary -- 7.5 Exercises -- 7.6 Notes and Sources -- 8. Markov Chains Applications -- 8.1 Bounds on Invariant Measures -- 8.2 Evaluation of Ergodic Criteria -- 8.3 Summary -- 8.4 Exercises -- 8.5 Notes and Sources -- 9. Application in Mathematical Finance -- 9.1 Option Pricing with Moment Information -- 9.2 Option Pricing with a Dynamic Model -- 9.3 Summary -- 9.4 Notes and Sources -- 10. Application in Control -- 10.1 Introduction -- 10.2 Weak Formulation of Optimal Control Problems -- 10.3 Semide finite Relaxations for the OCP -- 10.4 Summary -- 10.5 Notes and Sources -- 11. Convex Envelope and Representation of Convex Sets -- 11.1 The Convex Envelope of a Rational Function -- 11.2 Semide finite Representation of Convex Sets -- 11.3 Algebraic Certificates of Convexity -- 11.4 Summary -- 11.5 Exercises -- 11.6 Notes and Sources -- 12. Multivariate Integration -- 12.1 Integration of a Rational Function -- 12.2 Integration of Exponentials of Polynomials -- 12.3 Maximum Entropy Estimation. 
520 |a Many important applications in global optimization, algebra, probability and statistics, applied mathematics, control theory, financial mathematics, inverse problems, etc. can be modeled as a particular instance of the Generalized Moment Problem (GMP) . This book introduces a new general methodology to solve the GMP when its data are polynomials and basic semi-algebraic sets. This methodology combines semidefinite programming with recent results from real algebraic geometry to provide a hierarchy of semidefinite relaxations converging to the desired optimal value. Applied on appropriate cones. 
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650 6 |a Méthodes des moments (Statistique) 
650 6 |a Polynômes. 
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650 7 |a Polynomials  |2 fast 
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