7 citations · 9 across the 7 of their papers we have counts for
5 papers · 1 filter
Low-Rank Extragradient Methods for Scalable Semidefinite Optimization
Dan Garber, Atara Kaplan
We consider several classes of highly important semidefinite optimization problems that involve both a convex objective function (smooth or nonsmooth) and additional linear or nonl…
Efficiency of First-Order Methods for Low-Rank Tensor Recovery with the Tensor Nuclear Norm Under Strict Complementarity
Dan Garber, Atara Kaplan
We consider convex relaxations for recovering low-rank tensors based on constrained minimization over a ball induced by the tensor nuclear norm, recently introduced in \cite{tensor…
Low-Rank Mirror-Prox for Nonsmooth and Low-Rank Matrix Optimization Problems
Dan Garber, Atara Kaplan
Low-rank and nonsmooth matrix optimization problems capture many fundamental tasks in statistics and machine learning. While significant progress has been made in recent years in d…
Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems
Dan Garber, Atara Kaplan
Low-rank and nonsmooth matrix optimization problems capture many fundamental tasks in statistics and machine learning. While significant progress has been made in recent years in d…
On the Efficient Implementation of the Matrix Exponentiated Gradient Algorithm for Low-Rank Matrix Optimization
Dan Garber, Atara Kaplan
Convex optimization over the spectrahedron, i.e., the set of all real positive semidefinite matrices with unit trace, has important applications in machine learning, si…