Local search (optimization) and Pattern search (optimization)

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Difference between Local search (optimization) and Pattern search (optimization)

Local search (optimization) vs. Pattern search (optimization)

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

Similarities between Local search (optimization) and Pattern search (optimization)

Local search (optimization) and Pattern search (optimization) have 8 things in common (in Unionpedia): Hypersphere, Iterative method, Luus–Jaakola, Mathematical optimization, Normal distribution, Random optimization, Random search, Uniform distribution (continuous).

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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Iterative method

In computational mathematics, an iterative method is a mathematical procedure that uses an initial guess to generate a sequence of improving approximate solutions for a class of problems, in which the n-th approximation is derived from the previous ones.

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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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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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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.

Local search (optimization) and Uniform distribution (continuous) · Pattern search (optimization) and Uniform distribution (continuous) · See more »

The list above answers the following questions

What Local search (optimization) and Pattern search (optimization) have in common
What are the similarities between Local search (optimization) and Pattern search (optimization)

Local search (optimization) and Pattern search (optimization) Comparison

Local search (optimization) has 47 relations, while Pattern search (optimization) has 17. As they have in common 8, the Jaccard index is 12.50% = 8 / (47 + 17).

References

This article shows the relationship between Local search (optimization) and Pattern search (optimization). To access each article from which the information was extracted, please visit:

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