Randomized regularized extended Kaczmarz algorithms for tensor recovery
share
Randomized regularized Kaczmarz algorithms have recently been proposed to solve tensor recovery models with consistent linear measurements. In this work, we propose a novel algorithm based on the randomized extended Kaczmarz algorithm (which converges linearly in expectation to the unique minimum norm least squares solution of a linear system) for tensor recovery models with inconsistent linear measurements. We prove the linear convergence in expectation of our algorithm. Numerical experiments on a tensor least squares problem and a sparse tensor recovery problem are given to illustrate the theoretical results.
READ FULL TEXT
