Least Squared Conjugate Gradient
LSConjugateGradient ( int n , LinearForward * p , Vector <double>* data, int it, double tol , int verb) a constructor
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LSConjugateGradient (int n , LinearForward * p , Vector <double>* data, int it, double tol ) a constructor | |||||||||||||
Model <double> | optimizer ( Model <double>& m0) CGLS search starting from m0, and returns an optimum model | ||||||||||||
Model <long> | optimizer ( Model <long>& m0) CGLS search starting from m0, and returns an optimum model |
The conjugate gradient implemented here should be used for the solution of the normal equations $A^T.A.x = A^T.y$. It is coded such that the product $A^T.A$ is never performed, to avoid numerical instabilities and non sparse matrices. This procedure comes straight from the classical paper "Methods of conjugate gradients for solving linear systems:, 1952, NBS J. Research by Hesteness and Stiefel."
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(c)opyright by Malte Zöckler, Roland Wunderling