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Line 14: 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 operator. Iterative Shrinkage thresholding algorithm, [http://en.wikipedia.org/wiki/Landweber_iteration projected Landweber], projected gradient, [http://en.wikipedia.org/wiki/Alternating_projection alternating projections], [http://en.wikipedia.org/wiki/Alternating_direction_method_of_multipliers#Alternating_direction_method_of_multipliers alternating-direction method of multipliers], alternating split Bregman are special instances of proximal algorithms. Details of proximal methods are discussed in <ref> Line 107: [http://en.wikipedia.org/wiki/Alternating_projection Alternating Projection ] [http://en.wikipedia.org/wiki/Alternating_direction_method_of_multipliers#Alternating_direction_method_of_multipliers alternating-direction method of multipliers] Fast Iterative Shrinkage Thresholding Algorithm (FISTA)<ref> {{cite article \| last1="Beck \| first1=A \| last2=Teboulle \| first2 = M \| title="A fast iterative shrinkage-thresholding algorithm for linear inverse problems" \|journal=SIAM J. Imaging Science\|volume=2 \|year=2009\|pages=183-202}}</ref> == References == * {{cite book

Proximal gradient method: Difference between revisions