A STATISTICAL METHOD FOR REGULARIZING NONLINEAR INVERSE PROBLEMS
Explore this paper's citation graph
- Type
- article
- Published
- 2012-01-01
- Cited by
- 0
- References
- 19
- OpenAlex
- https://openalex.org/W55325297
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:12434672
Keywords
Tikhonov regularization, Inverse problem, Mathematics, Regularization (linguistics), Applied mathematics
References
- マグローヒル科学技術用語大辞典 = McGraw-Hill dictionary of scientific and technical terms
- Inverse problem theory - and methods for model parameter estimation
- Inverse problems in atmospheric constituent transport
- Parameter estimation and inverse problems
- A GCV based method for nonlinear ill-posed problems
- Numerical computing with MATLAB
- Discrete Inverse Problems: Insight and Algorithms
- Discontinuous parameter estimates with least squares estimators
- Inversion of Soil Conductivity Profiles from Electromagnetic Induction Measurements
- A Newton root-finding algorithm for estimating the regularization parameter for solving ill-conditioned least squares problems
- Parameter estimation: A new approach to weighting a priori information
- Linear regression analysis
- Practical optimization
- McGraw-Hill dictionary of scientific and technical terms (5th ed.)
- Discrete Inverse Problems
- Dynamic Data Assimilation: Synopsis
- Nonlinear Regression
Cited by
No citing papers recorded for this paper.
Related papers
- Optimization and Regularization of Nonlinear Least Squares Problems
- Regularization Parameter Estimation for Least Squares: A Newton method using the 2 -distribution
- An inexact Levenberg-Marquardt method for large sparse nonlinear least squres
- A Globally Convergent Method For Nonlinear Least-squares Problems Based On The Gauss-newton Model With Spectral Correction
- Parameter Estimation with Least-Squares Method for the Inverse Gaussian distribution Model Using Simplex and Quasi-Newton Optimization Methods
- A regularization method for constrained nonlinear least squares
- Nonlinear Least Squares Inversion of Reflection Coefficients Using Bayesian Regularization
- Regularization Parameter Estimation for Least Squares: Using the 2 -curve
- Unified model and ill-posed property of numerical iterative formula for solving nonlinear least squares problem