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Stochastic optimal control in infinite dimension dynamic programming and HJB equations /
Providing an introduction to stochastic optimal control in infinite dimension, this book gives a complete account of the theory of second-order HJB equations in infinite-dimensional Hilbert spaces, focusing on its applicability to associated stochastic optimal control problems. It features a general i...
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Main Authors: | , , |
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Corporate Author: | |
Format: | eBook |
Language: | English |
Published: |
Cham, Switzerland :
Springer,
2017.
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Series: | Probability theory and stochastic modelling ;
v. 82. |
Physical Description: |
1 online resource. |
Subjects: | |
Online Access: | SpringerLink - Click here for access |
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100 | 1 | |a Fabbri, Giorgio, |0 https://id.loc.gov/authorities/names/no2001082365 |e author. | |
245 | 1 | 0 | |a Stochastic optimal control in infinite dimension : |b dynamic programming and HJB equations / |c Giorgio Fabbri, Fausto Gozzi, Andrezej Święch ; with a contribution by Marco Fuhrman and Gianmario Tessitore. |
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490 | 1 | |a Probability theory and stochastic modelling ; |v 82. | |
504 | |a Includes bibliographical references and index. | ||
520 | |a Providing an introduction to stochastic optimal control in infinite dimension, this book gives a complete account of the theory of second-order HJB equations in infinite-dimensional Hilbert spaces, focusing on its applicability to associated stochastic optimal control problems. It features a general introduction to optimal stochastic control, including basic results (e.g. the dynamic programming principle) with proofs, and provides examples of applications. A complete and up-to-date exposition of the existing theory of viscosity solutions and regular solutions of second-order HJB equations in Hilbert spaces is given, together with an extensive survey of other methods, with a full bibliography. In particular, Chapter 6, written by M. Fuhrman and G. Tessitore, surveys the theory of regular solutions of HJB equations arising in infinite-dimensional stochastic control, via BSDEs. The book is of interest to both pure and applied researchers working in the control theory of stochastic PDEs, and in PDEs in infinite dimension. Readers from other fields who want to learn the basic theory will also find it useful. The prerequisites are: standard functional analysis, the theory of semigroups of operators and its use in the study of PDEs, some knowledge of the dynamic programming approach to stochastic optimal control problems in finite dimension, and the basics of stochastic analysis and stochastic equations in infinite-dimensional spaces. | ||
588 | 0 | |a Vendor-supplied metadata. | |
505 | 0 | |a Preface -- 1. Preliminaries on stochastic calculus in infinite dimensions -- 2. Optimal control problems and examples -- 3. Viscosity solutions -- 4. Mild solutions in spaces of continuous functions -- 5. Mild solutions in L2 spaces -- 6. HJB Equations through Backward Stochastic Differential Equations (by M. Fuhrman and G. Tessitore) -- Appendix A, B, C, D, E -- Bibliography. | |
650 | 0 | |a Stochastic processes |x Mathematical models. |0 https://id.loc.gov/authorities/subjects/sh2010114677. | |
650 | 0 | |a Stochastic models. |0 https://id.loc.gov/authorities/subjects/sh2005004376. | |
650 | 0 | |a Hamiltonian systems. |0 https://id.loc.gov/authorities/subjects/sh85058563. | |
650 | 0 | |a Hamilton-Jacobi equations. |0 https://id.loc.gov/authorities/subjects/sh85058558. | |
650 | 0 | |a Hilbert space. |0 https://id.loc.gov/authorities/subjects/sh85060803. | |
650 | 0 | |a Probabilities. |0 https://id.loc.gov/authorities/subjects/sh85107090. | |
650 | 0 | |a Mathematical optimization. |0 https://id.loc.gov/authorities/subjects/sh85082127. | |
650 | 0 | |a Distribution (Probability theory) |0 https://id.loc.gov/authorities/subjects/sh85038545. | |
650 | 0 | |a Differential equations, Partial. |0 https://id.loc.gov/authorities/subjects/sh85037912. | |
650 | 0 | |a Functional analysis. |0 https://id.loc.gov/authorities/subjects/sh85052312. | |
650 | 1 | 0 | |a Mathematics. |0 https://id.loc.gov/authorities/subjects/sh85082139. |
650 | 2 | 4 | |a Calculus of Variations and Optimal Control; Optimization. |
650 | 2 | 2 | |a Probability Theory. |0 https://id.nlm.nih.gov/mesh/D011338. |
650 | 2 | 0 | |a Stochastic processes. |0 https://id.loc.gov/authorities/subjects/sh85128181. |
650 | 2 | 0 | |a Differential equations, Partial. |0 https://id.loc.gov/authorities/subjects/sh85037912. |
650 | 2 | 4 | |a Systems Theory, Control. |
650 | 2 | 0 | |a Functional analysis. |0 https://id.loc.gov/authorities/subjects/sh85052312. |
650 | 6 | |a Optimisation mathématique. | |
650 | 6 | |a Distribution (Théorie des probabilités) | |
650 | 6 | |a Équations aux dérivées partielles. | |
650 | 6 | |a Analyse fonctionnelle. | |
650 | 6 | |a Processus stochastiques |x Modèles mathématiques. | |
650 | 6 | |a Modèles stochastiques. | |
650 | 6 | |a Systèmes hamiltoniens. | |
650 | 6 | |a Équations de Hamilton-Jacobi. | |
650 | 6 | |a Espace de Hilbert. | |
650 | 6 | |a Probabilités. | |
650 | 7 | |a Probability & statistics. |2 bicssc. | |
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650 | 7 | |a Calculus of variations. |2 bicssc. | |
650 | 7 | |a MATHEMATICS |x Probability & Statistics |x Stochastic Processes. |2 bisacsh. | |
650 | 7 | |a distribution (statistics-related concept) |2 aat. | |
650 | 7 | |a MATHEMATICS |x Applied. |2 bisacsh. | |
650 | 7 | |a probability. |2 aat. | |
650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh. | |
650 | 7 | |a Procesos estocásticos |x Modelos matemáticos. |2 embne. | |
650 | 7 | |a Functional analysis. |2 fast. | |
650 | 7 | |a Distribution (Probability theory) |2 fast. | |
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650 | 7 | |a Mathematical optimization. |2 fast. | |
650 | 7 | |a Probabilities. |2 fast. | |
650 | 7 | |a Stochastic models. |2 fast. | |
650 | 7 | |a Stochastic processes |x Mathematical models. |2 fast. | |
700 | 1 | |a Gozzi, Fausto, |0 https://id.loc.gov/authorities/names/n86024182 |e author. | |
700 | 1 | |a Święch, Andrezej, |e author. | |
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773 | 0 | |t Springer eBooks. | |
776 | 0 | 8 | |i Print version: |a Fabbri, Giorgio. |t Stochastic optimal control in infinite dimension. |d Cham, Switzerland : Springer, 2017 |z 3319530666 |z 9783319530666 |w (OCoLC)967364986. |
830 | 0 | |a Probability theory and stochastic modelling ; |0 https://id.loc.gov/authorities/names/no2014110414 |v v. 82. | |
880 | |6 520-00/(S |a Providing an introduction to stochastic optimal control in inαnite dimension, this book gives a complete account of the theory of second-order HJB equations in inαnite-dimensional Hilbert spaces, focusing on its applicability to associated stochastic optimal control problems. It features a general introduction to optimal stochastic control, including basic results (e.g. the dynamic programming principle) with proofs, and provides examples of applications. A complete and up-to-date exposition of the existing theory of viscosity solutions and regular solutions of second-order HJB equations in Hilbert spaces is given, together with an extensive survey of other methods, with a full bibliography. In particular, Chapter 6, written by M. Fuhrman and G. Tessitore, surveys the theory of regular solutions of HJB equations arising in inαnite-dimensional stochastic control, via BSDEs. The book is of interest to both pure and applied researchers working in the control theory of stochastic PDEs, and in PDEs in inαnite dimension. Readers from other αelds who want to learn the basic theory will also αnd it useful. The prerequisites are: standard functional analysis, the theory of semigroups of operators and its use in the study of PDEs, some knowledge of the dynamic programming approach to stochastic optimal control problems in αnite dimension, and the basics of stochastic analysis and stochastic equations in inαnite-dimensional spaces. | ||
880 | 0 | |6 505-00/(S |a Preface; Acknowledgements; Contents; About the Authors; 1 Preliminaries on Stochastic Calculus in Infinite Dimension; 1.1 Basic Probability; 1.1.1 Probability Spaces, σ-Fields; 1.1.2 Random Variables; 1.1.3 The Bochner Integral; 1.1.4 Expectation, Covariance and Correlation; 1.1.5 Conditional Expectation and Conditional Probability; 1.1.6 Gaussian Measures on Hilbert Spaces and the Fourier Transform; 1.2 Stochastic Processes and Brownian Motion; 1.2.1 Stochastic Processes; 1.2.2 Martingales; 1.2.3 Stopping Times; 1.2.4 Q-Wiener Processes; 1.2.5 Simple and Elementary Processes. | |
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