Elias Salomão Helou

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The Gradient Sampling method is a recently developed tool for solving unconstrained nonsmooth optimization problems. Using just first order information about the objective function, it generalizes the steepest descent method, one of the most classical methods to minimize a smooth function. This manuscript aims at determining under which circumstances one(More)
Recently, optimization problems involving nonsmooth and locally Lipschitz functions have been subject of investigation, and an innovative method known as Gradient Sampling has gained attention. Although the method has shown good results for important real problems, some drawbacks still remain unexplored. This study suggests modifications to the gradient(More)
Recently, specially crafted unidimensional optimization has been successfully used as line search to accelerate the overrelaxed and monotone fast iterative shrinkage-threshold algorithm (OMFISTA) for computed tomography. In this paper, we extend the use of fast line search to the monotone fast iterative shrinkage-threshold algorithm (MFISTA) and some of its(More)
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