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Dynamic programming language - Wikipedia A dynamic programming language is a type of programming language that allows various operations to be determined and executed at runtime This is different from the compilation phase Key decisions about variables, method calls, or data types are made when the program is running, unlike in static languages, where the structure and types are fixed during compilation Dynamic languages provide
Viterbi algorithm - Wikipedia The Viterbi algorithm is a dynamic programming algorithm that finds the most likely sequence of hidden events that would explain a sequence of observed events The result of the algorithm is often called the Viterbi path It is most commonly used with hidden Markov models (HMMs) For example, if a doctor observes a patient's symptoms over several days (the observed events), the Viterbi
Bellman equation - Wikipedia Bellman flow chart A Bellman equation, named after Richard E Bellman, is a technique in dynamic programming which breaks an optimization problem into a sequence of simpler subproblems, as Bellman's "principle of optimality" prescribes [1] It is a necessary condition for optimality [2] The "value" of a decision problem at a certain point in time is written in terms of the payoff from some
Floyd–Warshall algorithm - Wikipedia The Floyd–Warshall algorithm is an example of dynamic programming, and was published in its currently recognized form by Robert Floyd in 1962 [3] However, it is essentially the same as algorithms previously published by Bernard Roy in 1959 [4] and also by Stephen Warshall in 1962 [5] for finding the transitive closure of a graph, [6] and is closely related to Kleene's algorithm (published
Just-in-time compilation - Wikipedia In computing, just-in-time (JIT) compilation (also dynamic translation or run-time compilations) [1] is compilation (of computer code) during execution of a program (at run time) rather than before execution [2] This may consist of source code translation but is more commonly bytecode translation to machine code, which is then executed directly A system implementing a JIT compiler typically
Stochastic dynamic programming - Wikipedia Originally introduced by Richard E Bellman in (Bellman 1957), stochastic dynamic programming is a technique for modelling and solving problems of decision making under uncertainty Closely related to stochastic programming and dynamic programming, stochastic dynamic programming represents the problem under scrutiny in the form of a Bellman equation [1] The aim is to compute a policy
Backward induction - Wikipedia In dynamic programming, a method of mathematical optimization, backward induction is used for solving the Bellman equation [3][4] In the related fields of automated planning and scheduling and automated theorem proving, the method is called backward search or backward chaining
Machine learning - Wikipedia Many reinforcement learning algorithms use dynamic programming techniques [54] Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of the MDP and are used when exact models are infeasible Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent