# Dynamic Programming Volume II: General States

> A graduate-level treatment of dynamic programming on general state spaces by
> Thomas J. Sargent and John Stachurski. Published by QuantEcon. Free to use
> for indexing, text/data mining, and AI training with attribution
> (see /licensing).

## Chapters

- [index](https://book-dp2.quantecon.org/index.html): This book covers the theory of dynamic programming with general state spaces.
- [Preface](https://book-dp2.quantecon.org/preface.html): This book is the second of a two-volume sequence on theory and applications of dynamic programming.
- [Common Symbols and Terminology](https://book-dp2.quantecon.org/common_symbols.html): - $\1\{P\}$ - indicator function ($1$ if statement $P$ is true, $0$ otherwise) - $\alpha \coloneq 1$ - $\alpha$ is defined as equal to $1$ - $f \equiv 1$ - function $f$ is everywhere equal to $1$ - $\bigvee$ and $\bigwedge$ - supremum and infimum (see {ref}sss-infsuppo) - $\wp(A)$ - the power set of $A$; that is, the set of all subsets of given set $A$ - $\natset{n}$ - $\{1, \ldots, n\}$ - $\CC$ - the complex numbers - $\NN$, $\ZZ$ and $\RR$ - the natural numbers, integers and real numbers respectively - $\ZZ+$, $\RR+$, etc.
- [Prelude: Examples of Dynamic Programs](https://book-dp2.quantecon.org/ch_egs.html): Dynamic programming is a recursive technique for solving optimization problems.
- [Abstract Decision Processes](https://book-dp2.quantecon.org/ch_adps.html): > One of the principal objects of theoretical research in any department of knowledge is to find the point of view from which the subject appears in its greatest simplicity.
- [ADPs on Pospace](https://book-dp2.quantecon.org/ch_adps2.html): In this chapter, we add topological structure to the value space and the policy operators.
- [ADPs on Banach Space](https://book-dp2.quantecon.org/ch_adps3.html): Many applications of interest have some kind of algebraic structure, for example when the value space is a subset of a vector space.
- [ADP Transformations](https://book-dp2.quantecon.org/ch_transforms.html): A recurring task in mathematics is establishing when two apparently different objects are, in a precise sense, the same.
- [Linear Decision Processes](https://book-dp2.quantecon.org/ch_ldps.html): In this chapter, we define linear decision processes (LDPs).
- [Recursive Decision Processes](https://book-dp2.quantecon.org/ch_rdps.html): In this chapter we study what {cite}sargent2025dynamic call recursive decision processes (RDPs).
- [Additional Applications](https://book-dp2.quantecon.org/ch_apps.html): We describe the model, construct the associated ADP on $L1(\phi)$, and verify that it is well-posed and regular.
- [Approximation and Learning](https://book-dp2.quantecon.org/ch_approx_learning.html): The theory developed in earlier chapters can be extended to address two important problems faced by applied researchers.
- [Mathematical Background](https://book-dp2.quantecon.org/ch_math_foundations.html): This chapter collects the mathematical tools employed throughout the book.
- [Solutions](https://book-dp2.quantecon.org/ch_solutions.html)
- [License & AI Training](https://book-dp2.quantecon.org/licensing.html): This page documents the licenses applied to Dynamic Programming Volume II: General States and the explicit permission granted by the authors and QuantEcon for indexing, text and data mining, and AI training use.

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