Pedro Pérez-Aros (Universidad de Chile)
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This presentation introduces the Boosted Double-Proximal Subgradient Algorithm
(BDSA), a novel splitting method for tackling general structured nonsmooth and
nonconvex optimization problems expressed as sums and differences of composite
functions. BDSA combines subgradients derived from data and proximal steps,
augmented by a linesearch strategy to boost performance. We establish the
convergence of BDSA under the Kurdyka–Łojasiewicz property and analyze its
convergence rate. The talk concludes with numerical experiments illustrating the
algorithm’s effectiveness and robustness across various test scenarios.