37 citations · 80 across the 18 of their papers we have counts for
19 papers · 1 filter
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 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…
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…
Active-set Newton-MR methods for nonconvex optimization problems with bound constraints
Ernesto G. Birgin, Geovani N. Grapiglia, Diaulas S. Marcondes
This paper presents active-set methods for minimizing nonconvex twice-continuously differentiable functions subject to bound constraints. Within the faces of the feasible set, we e…
Sub-sampled Trust-Region Methods with Deterministic Worst-Case Complexity Guarantees
Max L. N. Goncalves, Geovani N. Grapiglia
In this paper, we develop and analyze sub-sampled trust-region methods for solving finite-sum optimization problems. These methods employ subsampling strategies to approximate the…
A Non-Monotone Line-Search Method for Minimizing Functions with Spurious Local Minima
Zohreh Aminifard, Geovani Nunes Grapiglia
In this paper, we propose a new non-monotone line-search method for smooth unconstrained optimization problems with objective functions that have many non-global local minimizers.…
Enhancing finite-difference based derivative-free optimization methods with machine learning
Timothé Taminiau, Estelle Massart, Geovani Nunes Grapiglia
Derivative-Free Optimization (DFO) involves methods that rely solely on evaluations of the objective function. One of the earliest strategies for designing DFO methods is to adapt…
Fully Adaptive Zeroth-Order Method for Minimizing Functions with Compressible Gradients
Geovani Nunes Grapiglia, Daniel McKenzie
We propose an adaptive zeroth-order method for minimizing differentiable functions with -Lipschitz continuous gradients. The method is designed to take advantage of the eventual…
Universal nonmonotone line search method for nonconvex multiobjective optimization problems with convex constraints
Maria Eduarda Pinheiro, Geovani Nunes Grapiglia
In this work we propose a general nonmonotone line-search method for nonconvex multi\-objective optimization problems with convex constraints. At the th iteration, the degree of…
TRFD: A derivative-free trust-region method based on finite differences for composite nonsmooth optimization
Dânâ Davar, Geovani Nunes Grapiglia
In this work we present TRFD, a derivative-free trust-region method based on finite differences for minimizing composite functions of the form , where is a black-…
First and zeroth-order implementations of the regularized Newton method with lazy approximated Hessians
Nikita Doikov, Geovani Nunes Grapiglia
In this work, we develop first-order (Hessian-free) and zero-order (derivative-free) implementations of the Cubically regularized Newton method for solving general non-convex optim…
Adaptive Third-Order Methods for Composite Convex Optimization
Geovani Nunes Grapiglia, Yurii Nesterov
In this paper we propose third-order methods for composite convex optimization problems in which the smooth part is a three-times continuously differentiable function with Lipschit…
A Generalized Worst-Case Complexity Analysis for Non-Monotone Line Searches
Geovani N. Grapiglia, Ekkehard W. Sachs
We study the worst-case complexity of a non-monotone line search framework that covers a wide variety of known techniques published in the literature. In this framework, the non-mo…
Tensor Methods for Finding Approximate Stationary Points of Convex Functions
Geovani Nunes Grapiglia, Yurii Nesterov
In this paper we consider the problem of finding -approximate stationary points of convex functions that are -times differentiable with -Hölder continuous th derivative…
On the Complexity of an Augmented Lagrangian Method for Nonconvex Optimization
Geovani N. Grapiglia, Ya-xiang Yuan
In this paper we study the worst-case complexity of an inexact Augmented Lagrangian method for nonconvex constrained problems. Assuming that the penalty parameters are bounded, we…
Tensor Methods for Minimizing Convex Functions with Hölder Continuous Higher-Order Derivatives
Geovani Nunes Grapiglia, Yurii Nesterov
In this paper we study -order methods for unconstrained minimization of convex functions that are -times differentiable () with -Hölder continuous th derivativ…