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arXiv · 1807.00387

Convergence rates for an inertial algorithm of gradient type associated to a smooth nonconvex minimization

Szilárd Csaba László
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Abstract

We investigate an inertial algorithm of gradient type in connection with the minimization of a nonconvex differentiable function. The algorithm is formulated in the spirit of Nesterov's accelerated convex gradient method. We show that the generated sequences converge to a critical point of the objective function, if a regularization of the objective function satisfies the Kurdyka-{\L}ojasiewicz property. Further, we provide convergence rates for the generated sequences and the function values formulated in terms of the {\L}ojasiewicz exponent.

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Szilárd Csaba László. 2018-07-01. Convergence rates for an inertial algorithm of gradient type associated to a smooth nonconvex minimization. https://arxiv.org/abs/1807.00387

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