Numerical Methods for Stochastic Control Problems in Continuous Time

Chapter 1 Introduction

Chapter 3 Controlled Markov Chains

Chapter 4 Dynamic Programming Equations

Chapter 5 The Markov Chain Approximation Method: Introduction

Chapter 6 Construction of the Approximating Markov Chain

Chapter 7 Computational Methods for Controlled Markov Chains

Chapter 8 The Ergodic Cost Problem: Formulation and Algorithms

Chapter 9 Heavy Traffic and Singular Control Problems: Examples and Markov Chain Approximations

Chapter 10 Weak Convergence and the Characterization of Processes

Chapter 11 Convergence Proofs

Chapter 12 Convergence for Reflecting Boundaries, Singular Control and Ergodic Cost Problems

Chapter 13 Finite Time Problems and Nonlinear Filtering

Chapter 14 Problems from the Calculus of Variations

Chapter 15 The Viscosity Solution Approach to Proving Convergence of Numerical Schemes