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https://issues.apache.org/jira/browse/MATH-177?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Luc Maisonobe resolved MATH-177.
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Resolution: Fixed
conjugate gradient and simplex method for linear problems have been added
the package is now in good shape, further tunings will be added as new issues
if needed
> Provide a general minimizing package with a classical Gauss-Newton algorithm
> ----------------------------------------------------------------------------
>
> Key: MATH-177
> URL: https://issues.apache.org/jira/browse/MATH-177
> Project: Commons Math
> Issue Type: Improvement
> Affects Versions: 2.0
> Reporter: Mick
> Assignee: Luc Maisonobe
> Fix For: 2.0
>
> Attachments: BrentMinimizer.java, UnivariateRealSolver.java,
> UnivariateRealSolverImpl.java
>
>
> Currently the math API provides least squares only method for minimizing
> (solving). The limitation to least-squares problems comes from the
> Levenberg-Marquardt algorithm. A more general minimizer (not for quadratic
> forms) could be implemented by refactoring this with a classical GN, steepest
> descent and also conjugate gradient. We could use them as a basis for some
> least-squares solvers (and also keep the very efficient and specialized
> Levenberg-Marquardt too).
> Based on email exchange with Luc Maisonobe entitled [math] Minimizer on
> 1/15/08.
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