An algorithm for solving sparse nonlinear least squares problems
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Publication:1822462
DOI10.1007/BF02239974zbMath0618.65050MaRDI QIDQ1822462
Publication date: 1987
Published in: Computing (Search for Journal in Brave)
numerical experimentspreconditioningconjugate gradient algorithmglobal and local convergencenonlinear least squares problemsGauss-Newton methodlarge sparse Jacobian matrixtrust region scheme
Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Numerical computation of solutions to systems of equations (65H10)
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Cites Work
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- A restricted trust region algorithm for unconstrained optimization
- Large sparse numerical optimization
- Convergence analysis of the general Gauss-Newton algorithm
- A quasi-Newton method with modification of one column per iteration
- On some versions of incomplete block-matrix factorization iterative methods
- On the eigenvalue distribution of a class of preconditioning methods
- On the rate of convergence of the preconditioned conjugate gradient method
- Incomplete block matrix factorization preconditioning methods. The ultimate answer?
- On solving nonlinear least-squares problems in case of rankdeficient Jacobians
- Parallel and sequential Kaczmarz methods for solving underdetermined nonlinear equations
- Solving systems of nonlinear equations by means of an accelerated successive orthogonal projections method
- Solution of sparse linear least squares problems using Givens rotations
- Truncated-Newton algorithms for large-scale unconstrained optimization
- A Comparison of Some Methods for Solving Sparse Linear Least-Squares Problems
- A robust incomplete Choleski-conjugate gradient algorithm
- The Conjugate Gradient Method and Trust Regions in Large Scale Optimization
- A fast and robust unconstrained optimization method requiring minimum storage
- Numerical Methods for Large Sparse Linear Least Squares Problems
- Globally convergent conjugate gradient algorithms
- An Algorithm for Least-Squares Estimation of Nonlinear Parameters
- Some Recent Approaches to Solving Large Residual Nonlinear Least Squares Problems
- Testing Unconstrained Optimization Software
- An Evaluation of Mathematical Software that Solves Nonlinear Least Squares Problems
- An Adaptive Nonlinear Least-Squares Algorithm
- LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
- Inexact Newton Methods
- Direct Secant Updates of Matrix Factorizations
- The Convergence of the Ben-Israel Iteration for Nonlinear Least Squares Problems
- Sparse Matrix Methods in Optimization
- Modification of a Quasi-Newton Method for Nonlinear Equations with a Sparse Jacobian
- Methods of conjugate gradients for solving linear systems
- A method for the solution of certain non-linear problems in least squares
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