11 papers
A Finite-Difference Trust-Region Method for Convexly Constrained Smooth Optimization
Dânâ Davar, Geovani Nunes Grapiglia
We propose a derivative-free trust-region method based on finite-difference gradient approximations for smooth optimization problems with convex constraints. For nonconvex problems…
Robust Sparse Identification of Nonlinear Dynamics via Least Trimmed Squares
Fabio Amaral, Geovani N. Grapiglia, Cassio M. Oishi
In this work, we propose a robust Sparse Identification of Nonlinear Dynamics (SINDy) pipeline for handling datasets corrupted by noise and outliers. The method decouples outlier f…
Riemannian optimization with finite-difference gradient approximations
Timothé Taminiau, Estelle Massart, Geovani Nunes Grapiglia
Derivative-free Riemannian optimization (DFRO) aims to minimize an objective function using only function evaluations, under the constraint that the decision variables lie on a Rie…
Complexity of quadratic penalty methods with adaptive accuracy under a PL condition for the constraints
Florentin Goyens, Geovani N. Grapiglia
We study the quadratic penalty method (QPM) for smooth nonconvex optimization problems with equality constraints. Assuming the constraint violation satisfies the PL condition near…
On the Complexity of Lower-Order Implementations of Higher-Order Methods
Nikita Doikov, Geovani Nunes Grapiglia
In this work, we propose a method for minimizing non-convex functions with Lipschitz continuous th-order derivatives, starting from . The method, however, only require…
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…