Abstract
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.
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This research has been partially supported by Rutgers University, RUTCOR, New Brunswick, NJ 08903, USA, and by the Memorial Fund of Wilhelm Kheilhau.
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Flåm, S.D. On finite convergence and constraint identification of subgradient projection methods. Mathematical Programming 57, 427–437 (1992). https://doi.org/10.1007/BF01581092
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DOI: https://doi.org/10.1007/BF01581092