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• W2997453817 abstract "We consider the problem of atomic optimization, where the notion of sparsity is generalized to meaning some linear combination of few atoms. The definition of atomic set is very broad; popular examples include the standard basis, low-rank matrices, overcomplete dictionaries, permutation matrices, orthogonal matrices, etc. The model of atomic optimization therefore includes problems coming from many fields, including statistics, signal processing, machine learning, computer vision and so on. Specifically, we consider the problem of maximizing a restricted strongly convex (or concave), smooth function restricted to a linear combination of atoms. We extend recent work that establish linear convergence rates of greedy algorithms on restricted strongly concave, smooth functions on vectors to the realm of general atomic sets, where the convergence rate involves a novel quantity: the sparse atomic condition number. This leads to the strongest known multiplicative approximation guarantees for various flavors of greedy algorithms for atomic optimization; in particular, we show that in many settings of interest the greedy algorithm can attain strong approximation guarantees while maintaining sparsity. Furthermore, we introduce a scheme for forward-backward algorithms that achieves the same approximation guarantees. Secondly, we define an alternate notion of weak submodularity, which we show is tightly related to the more familiar version that has been used to prove earlier linear convergence rates. We prove analogous multiplicative approximation guarantees using this alternate weak submodularity, and establish its distinct identity and applications." @default.
• W2997453817 created "2020-01-10" @default.
• W2997453817 creator A5076150896 @default.
• W2997453817 date "2019-12-26" @default.
• W2997453817 modified "2023-09-27" @default.
• W2997453817 title "Sparse Optimization on General Atomic Sets: Greedy and Forward-Backward Algorithms" @default.
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• W2997453817 hasPublicationYear "2019" @default.
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