4 papers
Subgradient Methods on Manifolds with Lower Bounded Curvature
G. C. Bento, J. X. Cruz Neto, J. O. Lopes +1
The subgradient method is a classical and foundational approach in non-smooth convex optimization; its simplicity, robustness, and role as a conceptual and algorithmic starting poi…
Convergence Rates for the Alternating Minimization Algorithm in Structured Nonsmooth and Nonconvex Optimization
Glaydston C. Bento, Boris S. Mordukhovich, Tiago S. Mota +1
This paper is devoted to developing the alternating minimization algorithm for problems of structured nonconvex optimization proposed by Attouch, Bolté, Redont, and Soubeyran in 2…
A Riemannian AdaGrad-Norm Method
Glaydston de C. Bento, Geovani N. Grapiglia, Mauricio S. Louzeiro +1
We propose a manifold AdaGrad-Norm method (\textsc{MAdaGrad}), which extends the norm version of AdaGrad (AdaGrad-Norm) to Riemannian optimization. In contrast to line-search schem…
Convergence of Descent Optimization Algorithms under Polyak-Åojasiewicz-Kurdyka Conditions
G. C. Bento, B. S. Mordukhovich, T. S. Mota +1
This paper develops a comprehensive convergence analysis for generic classes of descent algorithms in nonsmooth and nonconvex optimization under several conditions of the Polyak-Å…