A unified optimization view on generalized matching pursuit and Frank-Wolfe


Francesco Locatello, Rajiv Khanna, Michael Tschannen, and Martin Jaggi


Proc. of International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 860-868, Feb. 2017.

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Two of the most fundamental prototypes of greedy optimization are the matching pursuit and Frank-Wolfe algorithms. In this paper, we take a unified view on both classes of methods, leading to the first explicit convergence rates of matching pursuit methods in an optimization sense, for general sets of atoms. We derive sublinear (1/t) convergence for both classes on general smooth objectives, and linear convergence on strongly convex objectives, as well as a clear correspondence of algorithm variants. Our presented algorithms and rates are affine invariant, and do not need any incoherence or sparsity assumptions.

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Copyright Notice: © 2017 F. Locatello, R. Khanna, M. Tschannen, and M. Jaggi.

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