Class ExcessiveGapMethod

source code

            object --+        
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   bases.BaseAlgorithm --+    
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   bases.ExplicitAlgorithm --+
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  bases.IterativeAlgorithm --+
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bases.InformationAlgorithm --+
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                            ExcessiveGapMethod

Nesterov's excessive gap method for strongly convex functions.

Parameters
----------
output : Boolean. Whether or not to return extra output information. If
        output is True, running the algorithm will return a tuple with two
        elements. The first element is the found regression vector, and the
        second is the extra output information.

eps : Positive float. Tolerance for the stopping criterion.

info : List or tuple of utils.consts.Info. What, if any, extra run
        information should be stored. Default is an empty list, which means
        that no run information is computed nor returned.

max_iter : Non-negative integer. Maximum allowed number of iterations.

min_iter : Non-negative integer less than or equal to max_iter. Minimum
        number of iterations that must be performed. Default is 1.

The excessive gap method for strongly convex functions.

Parameters
----------
function : The function to minimise. It contains two parts, function.g
        is the strongly convex part and function.h is the smoothed part
        of the function.

beta : Numpy array. A start vector. This is normally not given, but
        left None, since the start vector is computed by the algorithm.