Safe Peeling for L0-Regularized Least-Squares with supplementary material
This work addresses computational efficiency for researchers and practitioners in optimization and sparse modeling, though it appears incremental as it builds on existing Branch-and-Bound frameworks.
The paper tackles the computational challenge of solving L0-regularized least-squares problems via Branch-and-Bound by introducing a 'safe peeling' method that tightens convex relaxations to enable more aggressive pruning. Numerical simulations demonstrate significant gains, including reduced nodes explored and faster solving times.
We introduce a new methodology dubbed ``safe peeling'' to accelerate the resolution of L0-regularized least-squares problems via a Branch-and-Bound (BnB) algorithm. Our procedure enables to tighten the convex relaxation considered at each node of the BnB decision tree and therefore potentially allows for more aggressive pruning. Numerical simulations show that our proposed methodology leads to significant gains in terms of number of nodes explored and overall solving time.s show that our proposed methodology leads to significant gains in terms of number of nodes explored and overall solving time.