575 citations · 929 across the 52 of their papers we have counts for
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Convergence for nonconvex ADMM, with applications to CT imaging
Rina Foygel Barber, Emil Y. Sidky
The alternating direction method of multipliers (ADMM) algorithm is a powerful and flexible tool for complex optimization problems of the form . ADMM e…
An equivalence between critical points for rank constraints versus low-rank factorizations
Wooseok Ha, Haoyang Liu, Rina Foygel Barber
Two common approaches in low-rank optimization problems are either working directly with a rank constraint on the matrix variable, or optimizing over a low-rank factorization so th…
Alternating minimization and alternating descent over nonconvex sets
Wooseok Ha, Rina Foygel Barber
We analyze the performance of alternating minimization for loss functions optimized over two variables, where each variable may be restricted to lie in some potentially nonconvex c…
Gradient descent with nonconvex constraints: local concavity determines convergence
Rina Foygel Barber, Wooseok Ha
Many problems in high-dimensional statistics and optimization involve minimization over nonconvex constraints-for instance, a rank constraint for a matrix estimation problem-but li…
MOCCA: mirrored convex/concave optimization for nonconvex composite functions
Rina Foygel Barber, Emil Y. Sidky
Many optimization problems arising in high-dimensional statistics decompose naturally into a sum of several terms, where the individual terms are relatively simple but the composit…