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 we show 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, we demonstrate that optimally active constraints are identified in finite time.This research has been partially supported by Rutgers University, RUTCOR, New Brunswick, NJ 08903, USA, and by the Memorial Fund of Wilhelm Kheilhau.