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Stochastic Controls: Hamiltonian Systems and HJB Equations: Stochastic Modelling and Applied Probability, cartea 43

Autor Jiongmin Yong, Xun Yu Zhou
en Limba Engleză Paperback – 27 sep 2012

În cadrul programelor de cercetare avansată în matematică aplicată și statistică, studiul controlului optimal reprezintă un pilon fundamental. Suntem de părere că lucrarea Stochastic Controls de Jiongmin Yong și Xun Yu Zhou reușește să umple un gol teoretic semnificativ, abordând direct întrebarea privind legătura dintre principiul maximului al lui Pontryagin și programarea dinamică a lui Bellman. Deși ambele metode vizează aceleași probleme de optimizare, literatura de specialitate le-a tratat adesea separat. Autorii demonstrează cum sistemele Hamiltoniene (bazate pe ecuații diferențiale stocastice) interacționează cu ecuațiile Hamilton-Jacobi-Bellman (ecuații cu derivate parțiale de ordinul al doilea).

Remarcăm structura riguroasă a volumului, care debutează cu o sinteză a calculului stocastic — de la integrale Itô la martingale — oferind astfel instrumentarul necesar pentru a naviga capitolele complexe despre existența controalelor optimale și mulțimile de accesibilitate. Comparabil cu Deterministic and Stochastic Optimal Control de Wendell H. Fleming în rigurozitate, volumul de față se distinge prin focalizarea pe relația formală dintre cele două paradigme de control, depășind termenii euristici utilizați în cercetările anterioare anilor '80.

Această abordare integrată este o continuare firească a preocupărilor autorilor, Jiongmin Yong explorând teme similare în Stochastic Linear-Quadratic Optimal Control Theory: Differential Games and Mean-Field Problems, unde se concentrează pe jocuri diferențiale. Față de Controlled Markov Processes and Viscosity Solutions, care pune accent pe soluțiile de viscozitate, lucrarea de față oferă o perspectivă sistemică asupra sistemelor Hamiltoniene stocastice, fiind o resursă esențială pentru înțelegerea fundamentelor matematice ale controlului în condiții de incertitudine.

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Specificații

ISBN-13: 9781461271543
ISBN-10: 1461271541
Pagini: 464
Ilustrații: XXII, 439 p.
Dimensiuni: 155 x 235 x 24 mm
Greutate: 0.64 kg
Ediția:Softcover reprint of the original 1st ed. 1999
Editura: Springer
Colecția Springer
Seria Stochastic Modelling and Applied Probability

Locul publicării:New York, NY, United States

Public țintă

Research

De ce să citești această carte

Această lucrare este recomandată cercetătorilor și studenților la doctorat care doresc o înțelegere profundă a fundamentelor matematice ale controlului stocastic. Cititorul câștigă o viziune unificată asupra celor două metode clasice de optimizare, beneficiind de un text care face trecerea de la calculul stocastic elementar la sisteme Hamiltoniene complexe. Este o resursă de referință pentru rigoarea cu care tratează ecuațiile HJB și condițiile de optimalitate.


Descriere scurtă

As is well known, Pontryagin's maximum principle and Bellman's dynamic programming are the two principal and most commonly used approaches in solving stochastic optimal control problems. * An interesting phenomenon one can observe from the literature is that these two approaches have been developed separately and independently. Since both methods are used to investigate the same problems, a natural question one will ask is the fol­ lowing: (Q) What is the relationship betwccn the maximum principlc and dy­ namic programming in stochastic optimal controls? There did exist some researches (prior to the 1980s) on the relationship between these two. Nevertheless, the results usually werestated in heuristic terms and proved under rather restrictive assumptions, which were not satisfied in most cases. In the statement of a Pontryagin-type maximum principle there is an adjoint equation, which is an ordinary differential equation (ODE) in the (finite-dimensional) deterministic case and a stochastic differential equation (SDE) in the stochastic case. The system consisting of the adjoint equa­ tion, the original state equation, and the maximum condition is referred to as an (extended) Hamiltonian system. On the other hand, in Bellman's dynamic programming, there is a partial differential equation (PDE), of first order in the (finite-dimensional) deterministic case and of second or­ der in the stochastic case. This is known as a Hamilton-Jacobi-Bellman (HJB) equation.

Cuprins

1. Basic Stochastic Calculus.- 1. Probability.- 2. Stochastic Processes.- 3. Stopping Times.- 4. Martingales.- 5. Itô’s Integral.- 6. Stochastic Differential Equations.- 2. Stochastic Optimal Control Problems.- 1. Introduction.- 2. Deterministic Cases Revisited.- 3. Examples of Stochastic Control Problems.- 4. Formulations of Stochastic Optimal Control Problems.- 5. Existence of Optimal Controls.- 6. Reachable Sets of Stochastic Control Systems.- 7. Other Stochastic Control Models.- 8. Historical Remarks.- 3. Maximum Principle and Stochastic Hamiltonian Systems.- 1. Introduction.- 2. The Deterministic Case Revisited.- 3. Statement of the Stochastic Maximum Principle.- 4. A Proof of the Maximum Principle.- 5. Sufficient Conditions of Optimality.- 6. Problems with State Constraints.- 7. Historical Remarks.- 4. Dynamic Programming and HJB Equations.- 1. Introduction.- 2. The Deterministic Case Revisited.- 3. The Stochastic Principle of Optimality and the HJB Equation.- 4. Other Propertiesof the Value Function.- 5. Viscosity Solutions.- 6. Uniqueness of Viscosity Solutions.- 7. Historical Remarks.- 5. The Relationship Between the Maximum Principle and Dynamic Programming.- 1. Introduction.- 2. Classical Hamilton-Jacobi Theory.- 3. Relationship for Deterministic Systems.- 4. Relationship for Stochastic Systems.- 5. Stochastic Verification Theorems.- 6. Optimal Feedback Controls.- 7. Historical Remarks.- 6. Linear Quadratic Optimal Control Problems.- 1. Introduction.- 2. The Deterministic LQ Problems Revisited.- 3. Formulation of Stochastic LQ Problems.- 4. Finiteness and Solvability.- 5. A Necessary Condition and a Hamiltonian System.- 6. Stochastic Riccati Equations.- 7. Global Solvability of Stochastic Riccati Equations.- 8. A Mean-variance Portfolio Selection Problem.- 9. Historical Remarks.- 7. Backward Stochastic Differential Equations.- 1. Introduction.- 2. Linear Backward Stochastic Differential Equations.- 3. Nonlinear Backward Stochastic Differential Equations.- 4. Feynman—Kac-Type Formulae.- 5. Forward—Backward Stochastic Differential Equations.- 6. Option Pricing Problems.- 7. Historical Remarks.- References.

Recenzii

From the reviews:
SIAM REVIEW
"The presentation of this book is systematic and self-contained…Summing up, this book is a very good addition to the control literature, with original features not found in other reference books. Certain parts could be used as basic material for a graduate (or postgraduate) course…This book is highly recommended to anyone who wishes to study the relationship between Pontryagin’s maximum principle and Bellman’s dynamic programming principle applied to diffusion processes."
MATHEMATICS REVIEW
This is an authoratative book which should be of interest to researchers in stochastic control, mathematical finance, probability theory, and applied mathematics. Material out of this book could also be used in graduate courses on stochastic control and dynamic optimization in mathematics, engineering, and finance curricula. Tamer Basar, Math. Review