A derivative-free nonlinear least squares solver for nonsmooth functions
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Publication:6588730
DOI10.1007/978-3-031-47859-8_4MaRDI QIDQ6588730
Publication date: 16 August 2024
derivative-free optimizationnonlinear least squaresnonsmooth problemspreconditioned subspace descentzero-order optimizationpseudorandom preconditioningstructured finite-difference directions
Numerical analysis (65-XX) Calculus of variations and optimal control; optimization (49-XX) Systems theory; control (93-XX) Operations research, mathematical programming (90-XX) Game theory, economics, finance, and other social and behavioral sciences (91-XX)
Cites Work
- Randomized preprocessing of homogeneous linear systems of equations
- Random gradient-free minimization of convex functions
- Gaussian elimination is not optimal
- Error analysis of algorithms for matrix multiplication and triangular decomposition using Winograd's identity
- Minimization of functions having Lipschitz continuous first partial derivatives
- Use of preconditioned Krylov subspaces in conjugate gradient-type methods for solving a nonlinear least squares problem
- Hybrid Krylov Methods for Nonlinear Systems of Equations
- Introduction to Derivative-Free Optimization
- Convergence Theory of Nonlinear Newton–Krylov Algorithms
- On a Class of Nonlinear Equation Solvers Based on the Residual Norm Reduction over a Sequence of Affine Subspaces
- A Local Convergence Theory for Combined Inexact-Newton/Finite-Difference Projection Methods
- Zeroth-order optimization with orthogonal random directions
- A Derivative-Free Nonlinear Least Squares Solver
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