On the global complexity of a derivative-free Levenberg-Marquardt algorithm via orthogonal spherical smoothing
DOI10.1007/S10915-024-02649-4zbMATH Open1547.65069MaRDI QIDQ6608067
[[Person:6111315|Author name not available (Why is that?)]], Jinyan Fan
Publication date: 19 September 2024
Published in: Journal of Scientific Computing (Search for Journal in Brave)
derivative-free optimizationsmoothing techniqueLevenberg-Marquardt methodglobal complexityprobabilistic gradient models
Numerical mathematical programming methods (65K05) Derivative-free methods and methods using generalized derivatives (90C56)
Cites Work
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- On a global complexity bound of the Levenberg-marquardt method
- More test examples for nonlinear programming codes
- On trust region methods for unconstrained minimization without derivatives
- Worst-case evaluation complexity of derivative-free nonmonotone line search methods for solving nonlinear systems of equations
- Levenberg-Marquardt method based on probabilistic Jacobian models for nonlinear equations
- A derivative-free Gauss-Newton method
- Random gradient-free minimization of convex functions
- Derivative free analogues of the Levenberg-Marquardt and Gauss algorithms for nonlinear least squares approximation
- Global complexity bound of the Levenberg–Marquardt method
- Convergence of Trust-Region Methods Based on Probabilistic Models
- A Derivative-Free Algorithm for Least-Squares Minimization
- Levenberg--Marquardt Methods Based on Probabilistic Gradient Models and Inexact Subproblem Solution, with Application to Data Assimilation
- Tensor Methods for Nonlinear Equations
- Testing Unconstrained Optimization Software
- Complexity and global rates of trust-region methods based on probabilistic models
- Benchmarking Derivative-Free Optimization Algorithms
- Scalable subspace methods for derivative-free nonlinear least-squares optimization
- Zeroth-order optimization with orthogonal random directions
- Stochastic Zeroth Order Gradient and Hessian Estimators: Variance Reduction and Refined Bias Bounds
This page was built for publication: On the global complexity of a derivative-free Levenberg-Marquardt algorithm via orthogonal spherical smoothing
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6608067)