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Bellman's contribution is remembered in the name of the Bellman equation, a central result of dynamic programming which restates an optimization problem in recursive form. Bellman explains the reasoning behind the term dynamic programming in his autobiography, Eye of the Hurricane: An Autobiography: I spent the Fall quarter (of 1950) at RAND ...
Adaptive Huffman coding (also called Dynamic Huffman coding) is an adaptive coding technique based on Huffman coding. It permits building the code as the symbols are being transmitted, having no initial knowledge of source distribution, that allows one-pass encoding and adaptation to changing conditions in data.
Adaptive mesh refinement (AMR) changes the spacing of grid points, to change how accurately the solution is known in that region. In the shallow water example, the grid might in general be spaced every few feet—but it could be adaptively refined to have grid points every few inches in places where there are large waves.
Adaptive algorithm – Algorithm that changes its behavior at the time it is run; Personalization – Using technology to accommodate the differences between individuals; Adaptive hypermedia – Hypermedia which varies output provided according to a model of the user; Content adaptation – Design approach for distribution to mixed environments
Adaptive control; Hierarchical control; Intelligent control; Optimal control; Dynamic programming; Robust control; Stochastic control; System dynamics, system analysis; Takens' theorem; Exponential dichotomy; Liénard's theorem; Krylov–Bogolyubov theorem; Krylov-Bogoliubov averaging method
Adaptive software development (ASD) is a software development process that grew out of the work by Jim Highsmith and Sam Bayer on rapid application development (RAD). It embodies the principle that continuous adaptation of the process to the work at hand is the normal state of affairs.
For example, the dynamic programming algorithms described in the next section require an explicit model, and Monte Carlo tree search requires a generative model (or an episodic simulator that can be copied at any state), whereas most reinforcement learning algorithms require only an episodic simulator.
The Held–Karp algorithm, also called the Bellman–Held–Karp algorithm, is a dynamic programming algorithm proposed in 1962 independently by Bellman [1] and by Held and Karp [2] to solve the traveling salesman problem (TSP), in which the input is a distance matrix between a set of cities, and the goal is to find a minimum-length tour that visits each city exactly once before returning to ...