LSPIA, (stochastic) gradient descent, and parameter correction
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Publication:2074869
DOI10.1016/j.cam.2021.113921zbMath1482.65024OpenAlexW3212731412MaRDI QIDQ2074869
Publication date: 11 February 2022
Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cam.2021.113921
Numerical computation using splines (65D07) Numerical smoothing, curve fitting (65D10) Artificial neural networks and deep learning (68T07) Spline approximation (41A15)
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Using low-rank approximations of gridded data for spline surface fitting ⋮ Hyperpower least squares progressive iterative approximation
Cites Work
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- On the progressive iteration approximation property and alternative iterations
- Fast B-spline curve fitting by L-BFGS
- Weighted progressive iteration approximation and convergence analysis
- Local progressive-iterative approximation format for blending curves and patches
- Totally positive bases and progressive iteration approximation
- Constructing iterative non-uniform \(B\)-spline curve and surface to fit data points
- Intrinsic parametrization for approximation
- Generalized B-splines' geometric iterative fitting method with mutually different weights
- The convergence of least-squares progressive iterative approximation for singular least-squares fitting system
- High-quality point sampling for B-spline fitting of parametric curves with feature recognition
- Implicit progressive-iterative approximation for curve and surface reconstruction
- On a progressive and iterative approximation method with memory for least square fitting
- A composite iterative procedure with fast convergence rate for the progressive-iteration approximation of curves
- Preconditioned progressive iterative approximation for triangular Bézier patches and its application
- An improved Hoschek intrinsic parametrization
- Progressive iterative approximation for regularized least square bivariate B-spline surface fitting
- Preconditioned progressive iterative approximation for tensor product Bézier patches
- Randomized Iterative Methods for Linear Systems
- Optimization Methods for Large-Scale Machine Learning
- Deep Learning: An Introduction for Applied Mathematicians
- An Efficient Method for Fitting Large Data Sets Using T-Splines
- Advanced surface fitting techniques
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