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Numerical Methods for Stochastic Control Problems in Continuous Time

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Cover of 'Numerical Methods for Stochastic Control Problems in Continuous Time'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Review of Continuous Time Models
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    Chapter 3 Controlled Markov Chains
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    Chapter 4 Dynamic Programming Equations
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    Chapter 5 The Markov Chain Approximation Method: Introduction
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    Chapter 6 Construction of the Approximating Markov Chain
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    Chapter 7 Computational Methods for Controlled Markov Chains
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    Chapter 8 The Ergodic Cost Problem: Formulation and Algorithms
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    Chapter 9 Heavy Traffic and Singular Control Problems: Examples and Markov Chain Approximations
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    Chapter 10 Weak Convergence and the Characterization of Processes
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    Chapter 11 Convergence Proofs
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    Chapter 12 Convergence for Reflecting Boundaries, Singular Control and Ergodic Cost Problems
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    Chapter 13 Finite Time Problems and Nonlinear Filtering
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    Chapter 14 Controlled Variance and Jumps
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    Chapter 15 Problems from the Calculus of Variations: Finite Time Horizon
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    Chapter 16 Problems from the Calculus of Variations: Infinite Time Horizon
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    Chapter 17 The Viscosity Solution Approach to Proving Convergence of Numerical Schemes
Attention for Chapter 4: Dynamic Programming Equations
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