Hsiao-Han Chao
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View article: Structured Low-Rank Matrix Approximation in Signal Processing: Semidefinite Formulations and Entropic First-Order Methods
Structured Low-Rank Matrix Approximation in Signal Processing: Semidefinite Formulations and Entropic First-Order Methods Open
Applications of semidefinite optimization in signal processing are often derived from the Kalman–Yakubovich–Popov lemma and its extensions, which give sum-of-squares theorems of nonnegative trigonometric polynomials and generalized polynomi…
View article: Semidefinite representations of gauge functions for structured low-rank matrix decomposition
Semidefinite representations of gauge functions for structured low-rank matrix decomposition Open
This paper presents generalizations of semidefinite programming formulations of 1-norm optimization problems over infinite dictionaries of vectors of complex exponentials, which were recently proposed for superresolution, gridless compress…