Mar 18, 2025 · Wherever we see a recursive solution that has repeated calls for the same inputs, we can optimize it using Dynamic Programming. The idea is to simply store the results of subproblems so that we do not have to re-compute them when needed later.

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.

The course covers the basic models and solution techniques for problems of sequential decision making under uncertainty (stochastic control). We will consider optimal control of a dynamical system over both a finite and an infinite number of stages.

The leading and most up-to-date textbook on the far-ranging algorithmic methododogy of Dynamic Programming, which can be used for optimal control, Markovian decision problems, planning and sequential decision making under uncertainty, and discrete/combinatorial optimization.

For systems with continuous states and continuous actions, dynamic programming is a set of theoretical ideas surrounding additive-cost optimal control problems. For systems with a finite, discrete set of states and a finite, discrete set of actions, dynamic programming also represents a set of very efficient numerical algorithms which can ...

Oct 31, 2021 · Dynamic programming is more complex and time-consuming; Control Abstraction. Dynamic programming (DP) splits the large problem at every possible point. When the problem becomes sufficiently small, DP solves it.

In this Chapter, we will introduce two problem setups where DP can in fact be implemented exactly.

2 days ago · In this paper, a new dynamic event-triggering (DET) mechanism based on adaptive dynamic programming (ADP) is developed to deal with the robust control problem of partially unknown uncertain systems. First, this paper completes the transition from the robust control problem to the optimal control problem by designing a nominal system.

6.231 Dynamic Programming and Stochastic Control. Fall 2015 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.

Concise representation of subsets of small integers {0, 1, . . .} – Does this make sense now? Remember the three steps!