7 citations · 9 across the 7 of their papers we have counts for
7 papers
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…
Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems
Dan Garber, Atara Kaplan
Composite convex optimization problems which include both a nonsmooth term and a low-rank promoting term have important applications in machine learning and signal processing, such…
Improved Complexities of Conditional Gradient-Type Methods with Applications to Robust Matrix Recovery Problems
Dan Garber, Shoham Sabach, Atara Kaplan
Motivated by robust matrix recovery problems such as Robust Principal Component Analysis, we consider a general optimization problem of minimizing a smooth and strongly convex loss…