This project implements Scaled Gradient Descent for low-rank matrix estimations Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent,
Scaled Subgradient Method for robust low-rank matrix estimations Low-Rank Matrix Recovery with Scaled Subgradient Methods: Fast and Robust Convergence Without the Condition Number,
and low-rank tensor estimations described in Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements.