About: Gauss–Newton algorithm

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The Gauss–Newton algorithm is used to solve non-linear least squares problems. It is a modification of Newton's method for finding a minimum of a function. Unlike Newton's method, the Gauss–Newton algorithm can only be used to minimize a sum of squared function values, but it has the advantage that second derivatives, which can be challenging to compute, are not required. Non-linear least squares problems arise, for instance, in non-linear regression, where parameters in a model are sought such that the model is in good agreement with available observations.

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rdfs:label	Gauss–Newton algorithm (en)
rdfs:comment	The Gauss–Newton algorithm is used to solve non-linear least squares problems. It is a modification of Newton's method for finding a minimum of a function. Unlike Newton's method, the Gauss–Newton algorithm can only be used to minimize a sum of squared function values, but it has the advantage that second derivatives, which can be challenging to compute, are not required. Non-linear least squares problems arise, for instance, in non-linear regression, where parameters in a model are sought such that the model is in good agreement with available observations. (en)
sameAs	Gauss–Newton algorithm
dbp:wikiPageUsesTemplate	dbt:Cite_book dbt:Math dbt:Mvar dbt:Isaac_Newton dbt:Optimization_algorithms dbt:Least_Squares_and_Regression_Analysis
Subject	Statistical algorithms Least squares Optimization algorithms and methods
thumbnail	wiki-commons:Special:FilePath/Regression_pic_assymetrique.gif?width=300
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prov:wasDerivedFrom	http://en.wikipedia.org/wiki/Gauss–Newton_algorithm?oldid=1036019284&ns=0
Wikipage page ID	1164753 (xsd:integer)
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Wikipage revision ID	1036019284 (xsd:integer)
Link from a Wikipage to another Wikipage	Local convergence Non-linear least squares Nonlinear regression Row and column vectors Wiley (publisher) Backtracking line search Body relative direction Moore–Penrose inverse Stationary point Taylor's theorem Broyden–Fletcher–Goldfarb–Shanno algorithm Carl Friedrich Gauss Statistical algorithms Jacobian matrix and determinant Least squares Optimization algorithms and methods Davidon–Fletcher–Powell formula QR decomposition Quasi-Newton method Wolfe conditions Cholesky decomposition Line search Linear approximation Linear least squares Trust region Condition number Conjugate gradient method Hessian matrix Newton's method Newton's method in optimization Parallel computing Sparse matrix Errors and residuals Function (mathematics) Gradient Gradient descent Descent direction Transpose Isaac Newton Iterative method Levenberg–Marquardt algorithm Maxima and minima Rate of convergence
has abstract	The Gauss–Newton algorithm is used to solve non-linear least squares problems. It is a modification of Newton's method for finding a minimum of a function. Unlike Newton's method, the Gauss–Newton algorithm can only be used to minimize a sum of squared function values, but it has the advantage that second derivatives, which can be challenging to compute, are not required. Non-linear least squares problems arise, for instance, in non-linear regression, where parameters in a model are sought such that the model is in good agreement with available observations. The method is named after the mathematicians Carl Friedrich Gauss and Isaac Newton, and first appeared in Gauss' 1809 work Theoria motus corporum coelestium in sectionibus conicis solem ambientum. (en)
foaf:isPrimaryTopicOf	http://en.wikipedia.org/wiki/Gauss–Newton_algorithm
is Wikipage redirect of	Gauss-Newton algorithm Gauss-newton algorithm Newton-Gauss Gauss-Newton Gauss-Newton method Gauss–Newton Gauss–Newton method
is Link from a Wikipage to another Wikipage of	List of statistics articles List of things named after Carl Friedrich Gauss List of things named after Isaac Newton Non-linear least squares Adept (C++ library) Global Positioning System List of numerical analysis topics Early life of Isaac Newton List of algorithms Newton's method Newton's method in optimization GNSS positioning calculation Gauss-Newton algorithm Gauss-newton algorithm Gradient descent Determination of equilibrium constants Evolutionary acquisition of neural topologies Expectation–maximization algorithm Models of neural computation Iteratively reweighted least squares Least squares Levenberg–Marquardt algorithm Multilateration Newton-Gauss Gauss-Newton Gauss-Newton method Gauss–Newton

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