2 edition of **Control with stochastic stopping time.** found in the catalog.

Control with stochastic stopping time.

A. Klinger

- 68 Want to read
- 12 Currently reading

Published
**1967**
by Rand Corporation in Santa Monica, Calif
.

Written in English

- Automatic control.,
- Stochastic processes.

**Edition Notes**

Series | Rand Corporation. Research memorandum -- RM-5342, Research memorandum (Rand Corporation) -- RM-5342.. |

The Physical Object | |
---|---|

Pagination | 17 p. |

Number of Pages | 17 |

ID Numbers | |

Open Library | OL16539478M |

Stochastic optimal control. Bellman equations. Control dependent value functions. Continuous time models. Optimal stopping. Games with Fixed terminal condition. Snell envelope. Continuous time models. Exercises. Mathematical finance. Stock price models. Up and down martingales. Cox-Ross-Rubinstein model. Black-Scholes-Merton model. Summing up, this book is a very good addition to the stochastic control literature ." (Jose-Luis Menaldi, SIAM Reviews, Vol. 47 (4), ) "In recent time optimal control in finance is connected with modelling of stock prices by Lévy processes and considering of different transaction costs.

Oksendal-Sulem's book entitled "Applied Stochastic Control of Jump Diffusions", for example, as comprehensive as it is, does not deal with value functions with time dependence. zation-and-control stochastic-calculus stochastic-differential-equations hamiltonian-mechanics. For your problem just ignore the jumps in the below book.): Authors: Bernt Oksendal, Agnes Sulem. Title: Applied Stochastic Control of Jump Diffusions. Chapter: 4. Combined Optimal Stopping and Stochastic Control of Jump Diffusions. Page: Second Edition.

In covering the stochastic optimization problem, the author presents the Bellman equation in great detail, covering both finite and infinite horizon problems. What makes this chapter so much better is that the majority of it is dedicated to applying the theory to economic applications, deriving the equations that govern the optimal control process. Get this from a library! Stochastic control theory: dynamic programming principle. [Makiko Nishio] -- This book offers a systematic introduction to the optimal stochastic control theory via the dynamic programming principle, which is a powerful tool to analyze control problems. First we consider.

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: Stochastic Control Theory: Dynamic Programming Principle (Probability Theory and Stochastic Modelling (72)) (): Nisio, Makiko: BooksBrand: Ingramcontent. This book contains an introduction to three topics in stochastic control: discrete time stochastic control, i. e., stochastic dynamic programming (Chapter 1), piecewise - terministic control problems (Chapter 3), and control of Ito diffusions (Chapter 4).

The chapters include treatments of optimal stopping by: This book provides a comprehensive introduction to stochastic control problems in discrete and continuous time.

The material is presented logically, beginning with the discrete-time case before proceeding to the stochastic continuous-time models. Central themes are dynamic programming in discrete time and HJB-equations in continuous time. In probability theory, in particular in the study of stochastic processes, a stopping time (also Markov time, Markov moment, optional stopping time or optional time) is a specific type of “random time”: a random variable whose value is interpreted as the time at which a given stochastic process exhibits a certain behavior of Control with stochastic stopping time.

book. A stopping time is often defined by a stopping rule, a. stochastic control and optimal stopping problems. The remaining part of the lectures focus on the more recent literature on stochastic control, namely stochastic target problems.

These problems are moti-vated by the superhedging problem in nancial mathematics. The chapter reviews the schemes whereby, in addition to control variables, optimal stopping is allowed. It also explains N-person stochastic differential games with perfect observation, stochastic differential games with stopping time, and stochastic differential games with partial observation.

The chapter describes a saddle point in pure. 1 Stopping Times Stopping Times: De nition Given a stochastic process X = fX n: n 0g, a random time ˝is a discrete random variable n 0gbe a stochastic process. A stopping time with respect to X is a random time such that for each n 0, the event f˝= ngis completely determined by.

This edition provides a more generalized treatment of the topic than does the earlier book Lectures on Stochastic Control Theory (ISI Lecture Notes 9), where time-homogeneous cases are dealt with.

Here, for finite time-horizon control problems, DPP was formulated as a one-parameter nonlinear semigroup, whose generator provides the HJB equation. Search in this book series.

Applications of Variational Inequalities in Stochastic Control. Edited by Alain Bensoussan, Jacques-Louis Lions. Vol Pages ii-vi, () Chapter 4 Stopping-Time and Stochastic Optimal Control Problems Pages Download PDF.

Chapter preview. This book was originally published by Academic Press inand republished by Athena Scientific in in paperback form. It can be purchased from Athena Scientific or it can be freely downloaded in scanned form ( pages, about 20 Megs). The book is a comprehensive and theoretically sound treatment of the mathematical foundations of stochastic optimal control of discrete-time systems.

I have co-authored a book, with Wendell Fleming, on viscosity solutions and stochastic control; Controlled Markov Processes and Viscosity Solutions, Springer-Verlag, (second edition in ), and authored or co-authored several articles on nonlinear partial differential equations, viscosity solutions, stochastic optimal control and.

Stochastic control or stochastic optimal control is a sub field of control theory that deals with the existence of uncertainty either in observations or in the noise that drives the evolution of the system.

The system designer assumes, in a Bayesian probability-driven fashion, that random noise with known probability distribution affects the evolution and observation of the state variables. A treatment of a continuous deterministic control process where only the interruption time is stochastic, and the uncertainty about the duration of the process is assumed to be summarized in a given cumulative probability distrubution for the stopping time.

The optimal control, minimum expected cost, and feedback rule are derived for a linear. This book offers a systematic introduction to the optimal stochastic control theory via the dynamic programming principle, which is a powerful tool to analyze control we consider completely observable control problems with finite horizons.

Using a time discretization we construct a. A class of stochastic control problems in contin uous time that lies between ordinary stopping problems and impulse control problems is giv en by multiple stopping prob- lems.

(shelved 1 time as stochastic-processes) avg rating — 1, ratings — published Want to Read saving. Besides finite time-horizon controls, the book discusses control-stopping problems in the same frameworks.

Optimal Stochastic Control, Stochastic Target Problems, and Backward SDE Nizar Touzi — Mathematics. In this paper, we study an optimal singular stochastic control problem. By using a time transformation, this problem is shown to be equivalent to an auxiliary control problem defined as a combination of an optimal stopping problem and a classical control problem.

By Huyen Pham, Continuous-time Stochastic Control and Optimization with Financial Applications. You can also get started with some lecture notes by the same author. This treatment is in much less depth: Page on This is the only bo. Introduction to Stopping Time in Stochastic Finance Theory. This book is an introduction to financial mathematics.

The first part of the book studies a simple one-period model which serves as. Stochastic Optimal Control and Optimization of Trading Algorithms. this out over time, and solve a stochastic control problem.

will seek for the optimal stopping time when unwinding the.This book incorporates an introduction to 3 subjects in stochastic control: discrete time stochastic control, i. e., stochastic dynamic programming (Chapter 1), piecewise – terministic control issues (Chapter three), and control of Ito diffusions (Chapter four).stochastic control problems in the next section.

One of the most useful expressions of this link is the following result, which may be regarded as a giant generalization of the classical mean-value theorem in classical analysis: The Dynkin formula Let X be a jump-di usion process and let ˝be a stopping time.

Let h 2C2(R) and assume that Ex R ˝.