- stochastic algorithms
- Макаров: стохастические алгоритмы
Универсальный англо-русский словарь. Академик.ру. 2011.
stochastic algorithms
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Stochastic optimization — (SO) methods are optimization algorithms which incorporate probabilistic (random) elements, either in the problem data (the objective function, the constraints, etc.), or in the algorithm itself (through random parameter values, random choices,… … Wikipedia
Stochastic approximation — methods are a family of iterative stochastic optimization algorithms that attempt to find zeroes or extrema of functions which cannot be computed directly, but only estimated via noisy observations. The first, and prototypical, algorithms of this … Wikipedia
Stochastic Diffusion Search — (SDS), was first described in 1989 as a population based, pattern matching algorithm [Bishop, 1989] . It belongs to a family of Swarm Intelligence and naturally inspired search and optimisation algorithms which includes Ant Colony Optimization,… … Wikipedia
Stochastic — (from the Greek Στόχος for aim or guess ) means random.A stochastic process is one whose behavior is non deterministic in that a state s next state is determined both by the process s predictable actions and by a random element. Stochastic crafts … Wikipedia
Stochastic gradient descent — is a general optimization algorithm, but is typically used to fit the parameters of a machine learning model.In standard (or batch ) gradient descent, the true gradient is used to update the parameters of the model. The true gradient is usually… … Wikipedia
Stochastic tunneling — (STUN) is an approach to global optimization based on the Monte Carlo method sampling of the function to be minimized. Idea Monte Carlo method based optimization techniques sample the objective function by randomly hopping from the current… … Wikipedia
Stochastic simulation — algorithms and methods were initially developed to analyse chemical reactions involving large numbers of species with complex reaction kinetics [cite journal |last=Bradley |first=Jeremy |authorlink=Jeremy Bradley |coauthors=Stephen Gilmore… … Wikipedia
Stochastic universal sampling — (SUS) is a genetic operator used in genetic algorithms for selecting potentially useful solutions for recombination.First introduced into the literature by Baker [1] , SUS is a development of Fitness proportionate selection which exhibits no bias … Wikipedia
Stochastic differential equation — A stochastic differential equation (SDE) is a differential equation in which one or more of the terms is a stochastic process, thus resulting in a solution which is itself a stochastic process. SDE are used to model diverse phenomena such as… … Wikipedia
Stochastic context-free grammar — A stochastic context free grammar (SCFG; also probabilistic context free grammar, PCFG) is a context free grammar in which each production is augmented with a probability. The probability of a derivation (parse) is then the product of the… … Wikipedia
Ant colony optimization algorithms — Ant behavior was the inspiration for the metaheuristic optimization technique. In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be… … Wikipedia
