Abstract
We investigate the convergence of subgradient-oriented descent methods in non-smooth non-convex optimization. We prove convergence in the sense of subsequences for functions with a strict standard model, and we show that convergence to a single critical point may be guaranteed if the Kurdyka–Łojasiewicz inequality is satisfied. We show, by way of an example, that the Kurdyka–Łojasiewicz inequality alone is not sufficient to prove the convergence to critical points.
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