A conjugate subgradient algorithm with adaptive preconditioning for LASSO minimization
Provides a faster optimization method for LASSO problems, benefiting practitioners in inverse problems and imaging.
The paper introduces a conjugate subgradient algorithm with adaptive preconditioning for LASSO minimization, achieving significant iteration reduction compared to state-of-the-art methods in ill-conditioned linear problems like CT with dictionary learning.
This paper describes a new efficient conjugate subgradient algorithm which minimizes a convex function containing a least squares fidelity term and an absolute value regularization term. This method is successfully applied to the inversion of ill-conditioned linear problems, in particular for computed tomography with the dictionary learning method. A comparison with other state-of-art methods shows a significant reduction of the number of iterations, which makes this algorithm appealing for practical use.