Cover Image

Enhancing finite-difference based derivative-free optimization methods with machine learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Taminiau, Timothé, Massart, Estelle, Grapiglia, Geovani Nunes
Format:	Preprint
Published:	2025
Subjects:	Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Riemannian optimization with finite-difference gradient approximations
by: Taminiau, Timothé, et al.
Published: (2026)

TRFD: A derivative-free trust-region method based on finite differences for composite nonsmooth optimization
by: Davar, Dânâ, et al.
Published: (2024)

Universal nonmonotone line search method for nonconvex multiobjective optimization problems with convex constraints
by: Pinheiro, Maria Eduarda, et al.
Published: (2024)

A Finite-Difference Trust-Region Method for Convexly Constrained Smooth Optimization
by: Davar, Dânâ, et al.
Published: (2025)

A Non-Monotone Line-Search Method for Minimizing Functions with Spurious Local Minima
by: Aminifard, Zohreh, et al.
Published: (2025)

On the Complexity of Lower-Order Implementations of Higher-Order Methods
by: Doikov, Nikita, et al.
Published: (2025)

Complexity of quadratic penalty methods with adaptive accuracy under a PL condition for the constraints
by: Goyens, Florentin, et al.
Published: (2026)

Fully Adaptive Zeroth-Order Method for Minimizing Functions with Compressible Gradients
by: Grapiglia, Geovani Nunes, et al.
Published: (2025)

Active-set Newton-MR methods for nonconvex optimization problems with bound constraints
by: Birgin, Ernesto G., et al.
Published: (2025)

Sub-sampled Trust-Region Methods with Deterministic Worst-Case Complexity Guarantees
by: Goncalves, Max L. N., et al.
Published: (2025)

Learning the subspace of variation for global optimization of functions with low effective dimension
by: Cartis, Coralia, et al.
Published: (2024)

A Riemannian AdaGrad-Norm Method
by: Bento, Glaydston de C., et al.
Published: (2025)

Sequential test sampling for stochastic derivative-free optimization
by: Ding, Anjie, et al.
Published: (2025)

A derivative-free particle method for optimization in Hilbert spaces
by: Huang, Hui, et al.
Published: (2026)

Gradient directions and relative inexactness in optimization and machine learning
by: Vasin, Artem
Published: (2024)

Randomized gradient-free methods in convex optimization
by: Gasnikov, Alexander, et al.
Published: (2022)

Exploiting higher-order derivatives in convex optimization methods
by: Kamzolov, Dmitry, et al.
Published: (2022)

On the optimal pivot path of simplex method for linear programming based on reinforcement learning
by: Li, Anqi, et al.
Published: (2022)

Simple linesearch-free first-order methods for nonconvex optimization
by: Yagishita, Shotaro, et al.
Published: (2025)

Exponential stability of finite-$N$ consensus-based optimization
by: Göttlich, Simone, et al.
Published: (2025)

Anderson acceleration of derivative-free projection methods for constrained monotone nonlinear equations
by: Jin, Jiachen, et al.
Published: (2024)

Curvature accelerated decentralized non-convex optimization for high-dimensional machine learning problems
by: Yi, Dingran, et al.
Published: (2025)

A derivative-free Levenberg-Marquardt method for sparse nonlinear least squares problems
by: Feng, Yuchen, et al.
Published: (2025)

Topology optimization for particle flow problems using Eulerian-Eulerian model with a finite difference method
by: Chen, Chih-Hsiang, et al.
Published: (2024)

A stochastic gradient method for trilevel optimization
by: Giovannelli, Tommaso, et al.
Published: (2025)

Complexity results and active-set identification of a derivative-free method for bound-constrained problems
by: Brilli, Andrea, et al.
Published: (2024)

Stratified adaptive sampling for derivative-free stochastic trust-region optimization
by: Amici, Giovanni, et al.
Published: (2026)

Optimal and parameter-free gradient minimization methods for convex and nonconvex optimization
by: Lan, Guanghui, et al.
Published: (2023)

A hybrid deep learning method for finite-horizon mean-field game problems
by: Zhang, Yu, et al.
Published: (2023)

Parameter-free proximal bundle methods with adaptive stepsizes for hybrid convex composite optimization problems
by: Monteiro, Renato D. C., et al.
Published: (2024)

Common pitfalls to avoid while using multiobjective optimization in machine learning
by: Akhter, Junaid, et al.
Published: (2024)

Stochastic set-valued optimization and its application to robust learning
by: Giovannelli, Tommaso, et al.
Published: (2026)

A black-box optimization method with polynomial-based kernels and quadratic-optimization annealing
by: Minamoto, Yuki, et al.
Published: (2025)

A model reduction method based on nonlinear optimization for multiscale stochastic optimal control problems
by: Ma, Lingling, et al.
Published: (2025)

An intrusion detection system in internet of things using grasshopper optimization algorithm and machine learning algorithms
by: Sattarpour, Shiva, et al.
Published: (2025)

Further extensions on the successive approximation method for hierarchical optimal control problems and its application to learning
by: Befekadu, Getachew K.
Published: (2024)

Minimization of eddy currents in permanent magnets of an electric machine with shape derivatives
by: Cesarano, Alessio, et al.
Published: (2025)

An interior point method for nonlinear constrained derivative-free optimization
by: Brilli, Andrea, et al.
Published: (2021)

On derivative-free sample-and-hold control with prescribed performance
by: Lanza, Lukas
Published: (2024)

Accelerated windowing for the crew rostering problem with machine learning
by: Racette, Philippe, et al.
Published: (2025)