- Referenced in 643 articles
- identification problem and a discretized optimal control problem, both of which are used to demonstrate ... method for the solution of nonlinear least squares problems. Both, overdetermined and underdetermined nonlinear least...
- Referenced in 525 articles
- solve linear equations and linear least-squares problems. The package solves linear systems whose matrices ... indefinite, symmetric positive definite, triangular, and tridiagonal square. In addition, the package computes ... rectangular matrices and applies them to least-squares problems. LINPACK uses column-oriented algorithms...
- Referenced in 421 articles
- solving systems of linear equations, least squares problems, and eigenvalue problems. The goals of both ... factorization, matrix inversion, full-rank linear least squares problems, orthogonal and generalized orthogonal factorizations, orthogonal...
- Referenced in 490 articles
- singular value decomposition to solve certain least-squares problems. EISPACK has been superseded...
- Referenced in 410 articles
- LSQR: Sparse Linear Equations and Least Squares Problems. An iterative method is given for solving...
- Referenced in 1713 articles
- linear equations, least-squares solutions of linear systems of equations, eigenvalue problems, and singular value...
- Referenced in 199 articles
- norm solution of an underdetermined least-squares problem. Basis Pursuit DeNoise (BPDN) fits the least ... squares problem only approximately, and a single parameter determines a curve that traces the optimal ... trade-off between the least-squares fit and the one-norm of the solution ... gradient-projection method approximately minimizes a least-squares problem with an explicit one-norm constraint...
- Referenced in 231 articles
- total least squares problem: computational aspects and analysis. Total least squares...
- Referenced in 113 articles
- linear systems of equations and linear least squares problems, in particular for systems with multiple...
- Referenced in 76 articles
- LSMR: an iterative algorithm for sparse least-squares problems. An iterative method LSMR is presented ... solving linear systems Ax=b and least-squares problems min∥Ax-b∥ 2 , with...
Harwell-Boeing sparse matrix collection
- Referenced in 217 articles
- test set comprises problems in linear systems, least squares, and eigenvalue calculations from a wide ... variety of scientific and engineering disciplines. The problems range from small matrices, used as counter...
- Referenced in 90 articles
- including systems of linear equations, linear least squares problems, eigenvalue problems, and singular value problems...
- Referenced in 71 articles
- invert a matrix, to solve least squares problems, to perform unconstrained minimization, to compute eigenvalues...
- Referenced in 70 articles
- MATLAB solver for the l1-regularized least squares problem: A fast algorithm for sparse reconstruction...
- Referenced in 66 articles
- become a standard technique for nonlinear least-squares problems and can be thought...
- Referenced in 312 articles
- Solve linear, quadratic, integer, and nonlinear optimization problems. Optimization Toolbox™ provides functions for finding parameters ... quadratic programming, nonlinear optimization, and nonlinear least squares. You can use these solvers to find ... optimal solutions to continuous and discrete problems, perform tradeoff analyses, and incorporate optimization methods into...
- Referenced in 42 articles
- really solving one of the least-squares problems minimize ... MINRES returns a least-squares solution with small ||Ar|| (where...
- Referenced in 39 articles
- linear equations and large sparse least squares problems...
- Referenced in 28 articles
- symmetric and Hermitian linear equations and least-squares problems. We describe algorithm MINRES ... symmetric or Hermitian linear systems or least-squares problems. If the system is singular, MINRES...
- Referenced in 29 articles
- Jellyfish include matrix completion problems and least-squares problems regularized by the nuclear norm...