17 citations · 28 across the 7 of their papers we have counts for
4 papers · 1 filter
On Local Linear Convergence of Projected Gradient Descent for Unit-Modulus Least Squares
Trung Vu, Raviv Raich, Xiao Fu
The unit-modulus least squares (UMLS) problem has a wide spectrum of applications in signal processing, e.g., phase-only beamforming, phase retrieval, radar code design, and sensor…
On Asymptotic Linear Convergence Rate of Iterative Hard Thresholding for Matrix Completion
Trung Vu, Evgenia Chunikhina, Raviv Raich
Iterative hard thresholding (IHT) has gained in popularity over the past decades in large-scale optimization. However, convergence properties of this method have only been explored…
On Asymptotic Linear Convergence of Projected Gradient Descent for Constrained Least Squares
Trung Vu, Raviv Raich
Many recent problems in signal processing and machine learning such as compressed sensing, image restoration, matrix/tensor recovery, and non-negative matrix factorization can be c…
Exact Linear Convergence Rate Analysis for Low-Rank Symmetric Matrix Completion via Gradient Descent
Trung Vu, Raviv Raich
Factorization-based gradient descent is a scalable and efficient algorithm for solving low-rank matrix completion. Recent progress in structured non-convex optimization has offered…