  Numerous problems in machine learning are formulated as optimization with
manifold constraints. In this paper, we propose the Manifold alternating
directions method of multipliers (MADMM), an extension of the classical ADMM
scheme for manifold-constrained non-smooth optimization problems and show its
application to several challenging problems in dimensionality reduction, data
analysis, and manifold learning.
