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where some of the functions are non-differentiable, this rules out our conventional smooth optimization techniques like
[[Gradient descent|Steepest descent method]], [[conjugate gradient method]] etc. There is a specific class of [[algorithms]] which can solve the 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 [[wikt:implementable|implementable]]
They are called [[proximal]] because each non [[smooth function]] among <math>f_1, . . . , f_n</math> is involved via its proximity
operator. Iterative Shrinkage thresholding algorithm, [[Landweber iteration|projected Landweber]], projected
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