Dynamic Programming: Finite States by Thomas Sargent (.PDF)+

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Dynamic Programming: Finite States (2025) by Thomas Sargent, John Stachurski
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Overview: Dynamic Programming is an algorithmic technique with the following properties. It is mainly an optimization over plain recursion. Wherever we see a recursive solution that has repeated calls for the same inputs, we can optimize it using Dynamic Programming. Dynamic Programming: Finite States treats the theory of dynamic programming and its applications in economics, finance, and operations research. It contains classical results on dynamic programming as well as extensions created by researchers and practitioners as they wrestle with formulating and solving dynamic models that can explain patterns observed in data. Adopting an abstract framework that provides great generality, this book facilitates rapid progress to the research frontier by combining rigorous theory with numerous applications, many solved exercises, and detailed open-source computer code. Code is written in Julia and can be found at GitHub. We chose Julia because it is open source and because Julia allows us to write computer code that is as close as possible to the relevant mathematical equations. Julia code in the text is written to maximize clarity rather than speed. We have also written matching Python code that can be found in the same repository. When combined with appropriate scientific libraries, Python is very practical and efficient for dynamic programming, but implementations tend to be library specific and are sometimes not as clean as those in Julia. That is why we chose Julia for programs embedded in the text.
Genre: Non-Fiction > Tech & Devices

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