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Dynamic programming is both a mathematical optimization method and an algorithmic paradigm. The method was developed by Richard Bellman in the 1950s and has found applications in numerous fields, from aerospace engineering to economics.
Dynamic Optimization is a carefully presented textbook which starts with discrete-time deterministic dynamic optimization problems, providing readers with the tools for sequential decision-making, before proceeding to the more complicated stochastic models.
This chapter provides an introduction to the subject of dynamic optimization theory, which is used in economics to address various intertemporal issues. It covers the history, methods, and applications of dynamic optimization models, with a focus on the Ramsey problem and the turnpike property.
27 wrz 2021 · Learn about dynamic optimization problems in continuous and discrete time, with examples from engineering, economics, and business. The chapter covers variational calculus, optimal control, dynamic programming, and numerical methods.
A book that covers the modern theory of dynamic optimization, incorporating recent advances in nonsmooth analysis, differential inclusions, and dynamic programming. It offers a self-contained exposition of the Hamilton-Jacobi equation, necessary conditions, minimizer regularity, and global optimality conditions.
Learn dynamic optimization methods in discrete and continuous time, with applications in economics and engineering. This course covers dynamic programming, optimal control, and dynamic systems, with notes, problem sets, and exams.
Learn about the mathematical methods for dynamic optimization, such as the calculus of variations and optimal control theory. This web page provides the notes for the spring 2021 version of Math 195, taught by Lawrence C. Evans at UC Berkeley.
