Proximal gradient method: Difference between revisions

Revision as of 14:33, 4 March 2014

Revision as of 11:45, 9 April 2014

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where <math>f_1, f_2, ..., f_n </math> are [[convex functions]] defined from <math>f: \mathbb{R}^N \rightarrow \mathbb{R} </math>

where some of the functions are non-differentiable, this rules out our conventional smooth optimization techniques like

[[Gradient descent|Steepest decentdescent method]], [[conjugate gradient method]] etc. There is a specific class of [[algorithms]] which can solve above optimization problem. These methods proceed by splitting,

in that the functions <math>f_1, . . . , f_n</math> are used individually so as to yield an easily [[implementable]] algorithm.

They are called [[proximal]] because each non [[smooth function]] among <math>f_1, . . . , f_n</math> is involved via its proximity