On finite convergence and constraint identification of subgradient projection methods

This paper deals with a continuous time, subgradient projection algorithm, shown to generate trajectories that accumulate to the solution set. Under a strong convexity assumption it is shown that convergence is exponential in norm. A sharpness condition yields convergence in finite time, and the necessary lapse is estimated. Invoking a constraint qualification and a non-degeneracy assumption, it is demonstrated that optimally active constraints are identified in finite time.