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2014

Self Equivalence of the Alternating Direction Method of Multipliers

Yan, Ming, Yin, Wotao

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The alternating direction method of multipliers (ADM or ADMM) breaks a complex optimization problem into much simpler subproblems.

The ADM algorithms are typically short and easy to implement yet exhibit (nearly) state-of-the-art performance for large-scale optimization problems.
To apply ADM, we first formulate a given problem into the "ADM-ready" form, so the final algorithm depends on the formulation.
A problem like $\mbox{minimize}_\mathbf{x} u(\mathbf{x}) + v(\mathbf{C}\mathbf{x})$ has six different "ADM-ready" formulations.

Self Equivalence of the Alternating Direction Method of Multipliers · Around

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