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Random optimization

Index Random optimization

Random optimization (RO) is a family of numerical optimization methods that do not require the gradient of the problem to be optimized and RO can hence be used on functions that are not continuous or differentiable. [1]

15 relations: Continuous function, Differentiable function, Evolution strategy, Gradient, Hypersphere, Limit (mathematics), Luus–Jaakola, Mathematical optimization, Normal distribution, Pattern search (optimization), Probability distribution, Random search, Stochastic optimization, Uniform distribution (continuous), Unimodality.

Continuous function

In mathematics, a continuous function is a function for which sufficiently small changes in the input result in arbitrarily small changes in the output.

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Differentiable function

In calculus (a branch of mathematics), a differentiable function of one real variable is a function whose derivative exists at each point in its domain.

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Evolution strategy

In computer science, an evolution strategy (ES) is an optimization technique based on ideas of evolution.

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Gradient

In mathematics, the gradient is a multi-variable generalization of the derivative.

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Hypersphere

In geometry of higher dimensions, a hypersphere is the set of points at a constant distance from a given point called its center.

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Limit (mathematics)

In mathematics, a limit is the value that a function (or sequence) "approaches" as the input (or index) "approaches" some value.

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Luus–Jaakola

In computational engineering, Luus–Jaakola (LJ) denotes a heuristic for global optimization of a real-valued function.

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Mathematical optimization

In mathematics, computer science and operations research, mathematical optimization or mathematical programming, alternatively spelled optimisation, is the selection of a best element (with regard to some criterion) from some set of available alternatives.

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Normal distribution

In probability theory, the normal (or Gaussian or Gauss or Laplace–Gauss) distribution is a very common continuous probability distribution.

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Pattern search (optimization)

Pattern search (also known as direct search, derivative-free search, or black-box search) is a family of numerical optimization methods that does not require a gradient.

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Probability distribution

In probability theory and statistics, a probability distribution is a mathematical function that provides the probabilities of occurrence of different possible outcomes in an experiment.

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Random search

Random search (RS) is a family of numerical optimization methods that do not require the gradient of the problem to be optimized, and RS can hence be used on functions that are not continuous or differentiable.

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Stochastic optimization

Stochastic optimization (SO) methods are optimization methods that generate and use random variables.

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Uniform distribution (continuous)

In probability theory and statistics, the continuous uniform distribution or rectangular distribution is a family of symmetric probability distributions such that for each member of the family, all intervals of the same length on the distribution's support are equally probable.

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Unimodality

In mathematics, unimodality means possessing a unique mode.

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Redirects here:

Random optimisation, Randomized optimization.

References

[1] https://en.wikipedia.org/wiki/Random_optimization

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