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- solving systems of simultaneous linear equations, least-squares solutions of linear systems of equations, eigenvalue...
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- method for the solution of nonlinear least squares problems. Both, overdetermined and underdetermined nonlinear least...
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- 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...
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- 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...
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- object, and finally performing verification through least-squares solution for consistent pose parameters. This approach...
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- singular value decomposition to solve certain least-squares problems. EISPACK has been superseded...
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- LSQR: Sparse Linear Equations and Least Squares Problems. An iterative method is given for solving...
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- total least squares problem: computational aspects and analysis. Total least squares...
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- quadratic programming, nonlinear optimization, and nonlinear least squares. You can use these solvers to find...
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- random number generators, special functions and least-squares fitting. There are over 1000 functions...
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- 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 ... 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...
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- analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates...
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- software and services, from linear systems, least squares, and eigenvalue computations in a wide variety...
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- including systems of linear equations, linear least squares problems, eigenvalue problems, and singular value problems...
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- point Jacobi, block Jacobi, Gauss-Seidel, least-squares polynomials, and overlapping domain decomposition using sparse...
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- When the problem is singular, a least-squares solution is computed. Singularities induced...
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- models, the Buckley-James model, generalized least squares for serially or spatially correlated observations, generalized...
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- invert a matrix, to solve least squares problems, to perform unconstrained minimization, to compute eigenvalues...