Search Results for author:
Found 6 papers, 0 papers with code
Efficiency of First-Order Methods for Low-Rank Tensor Recovery with the Tensor Nuclear Norm Under Strict Complementarity
For a smooth objective function, when initialized in certain proximity of an optimal solution which satisfies SC, standard projected gradient methods only require SVD computations (for projecting onto the tensor nuclear norm ball) of rank that matches the tubal rank of the optimal solution.
Low-Rank Mirror-Prox for Nonsmooth and Low-Rank Matrix Optimization Problems
Low-rank and nonsmooth matrix optimization problems capture many fundamental tasks in statistics and machine learning.
Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems
Low-rank and nonsmooth matrix optimization problems capture many fundamental tasks in statistics and machine learning.
On the Efficient Implementation of the Matrix Exponentiated Gradient Algorithm for Low-Rank Matrix Optimization
In this work we propose an efficient implementations of MEG, both with deterministic and stochastic gradients, which are tailored for optimization with low-rank matrices, and only use a single low-rank SVD computation on each iteration.
Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems
However, such problems are highly challenging to solve in large-scale: the low-rank promoting term prohibits efficient implementations of proximal methods for composite optimization and even simple subgradient methods.
Improved Complexities of Conditional Gradient-Type Methods with Applications to Robust Matrix Recovery Problems
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 function applied to the sum of two blocks of variables, where each block of variables is constrained or regularized individually.
