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Pattern search (optimization)

Index 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. [1]

13 relations: Broyden–Fletcher–Goldfarb–Shanno algorithm, Derivative-free optimization, Line search, List of numerical analysis topics, Local search (optimization), Luus–Jaakola, Mathematical optimization, Nelder–Mead method, Pattern search, Random optimization, Random search, Rosenbrock methods, Search optimization.

Broyden–Fletcher–Goldfarb–Shanno algorithm

In numerical optimization, the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm is an iterative method for solving unconstrained nonlinear optimization problems.

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Derivative-free optimization

Derivative-free optimization is a discipline in mathematical optimization that does not use derivative information in the classical sense to find optimal solutions: Sometimes information about the derivative of the objective function f is unavailable, unreliable or impractical to obtain.

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Line search

In optimization, the line search strategy is one of two basic iterative approaches to find a local minimum \mathbf^* of an objective function f:\mathbb R^n\to\mathbb R. The other approach is trust region.

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List of numerical analysis topics

This is a list of numerical analysis topics.

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Local search (optimization)

In computer science, local search is a heuristic method for solving computationally hard optimization problems.

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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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Nelder–Mead method

The Nelder–Mead method or downhill simplex method or amoeba method is a commonly applied numerical method used to find the minimum or maximum of an objective function in a multidimensional space.

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Pattern search

Pattern search may refer to.

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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.

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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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Rosenbrock methods

Rosenbrock methods refers to either of two distinct ideas in numerical computation, both named for Howard H. Rosenbrock.

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Search optimization

Search optimization may refer to.

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References

[1] https://en.wikipedia.org/wiki/Pattern_search_(optimization)

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