Solving Large Scale Quadratic Constrained Basis Pursuit
This addresses computational efficiency for large-scale optimization problems in fields like signal processing and machine learning, though it appears incremental as it builds on existing methods.
The authors tackled the problem of solving large-scale quadratically constrained basis pursuit by proposing an efficient algorithm based on alternating direction method of multipliers and operator splitting, achieving 50-100 times speedup compared to a baseline interior point method.
Inspired by alternating direction method of multipliers and the idea of operator splitting, we propose a efficient algorithm for solving large-scale quadratically constrained basis pursuit. Experimental results show that the proposed algorithm can achieve 50~~100 times speedup when compared with the baseline interior point algorithm implemented in CVX.