NonLinearConjugateGradientOptimizer line search

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NonLinearConjugateGradientOptimizer line search

Bruce A Johnson-2
The NonLinearConjugateGradientOptimizer does a line search for a zero in the gradient (see comment from source below), rather than a search for a minimum of the function (the latter is what is used in Numerical Recipes and in the simple discussion on Wikipedia ( ).  Is this wise?  It seems a clever idea, but  in a complicated surface with numerical errors the zero in the gradient may not be at a function minimum and the algorithm could be a deoptimizer.  I ask because (in a problem too complex too easily reproduce) I'm sometimes getting junk as output of this routine.


Comment for the LIneSearchFunction

350     * The function represented by this class is the dot product of
351     * the objective function gradient and the search direction. Its
352     * value is zero when the gradient is orthogonal to the search
353     * direction, i.e. when the objective function value is a local
354     * extremum along the search direction.

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