`scipy.optimize._lsq.trf_linear:regularized_lsq_with_qr`

source: /scipy/optimize/_lsq/trf_linear.py :18

Signature

   def     regularized_lsq_with_qr (    m    ,    n    ,    R    ,    QTb    ,    perm    ,    diag    ,    copy_R    =  True     )

Summary

Solve regularized least squares using information from QR-decomposition.

Extended Summary

The initial problem is to solve the following system in a least-squares sense

A x = b
D x = 0

where D is diagonal matrix. The method is based on QR decomposition of the form A P = Q R, where P is a column permutation matrix, Q is an orthogonal matrix and R is an upper triangular matrix.

Parameters

m, n : int: Initial shape of A.
R : ndarray, shape (n, n): Upper triangular matrix from QR decomposition of A.
QTb : ndarray, shape (n,): First n components of Q^T b.
perm : ndarray, shape (n,): Array defining column permutation of A, such that ith column of P is perm[i]-th column of identity matrix.
diag : ndarray, shape (n,): Array containing diagonal elements of D.

Returns

x : ndarray, shape (n,): Found least-squares solution.

Aliases

scipy.optimize._lsq.trf_linear.regularized_lsq_with_qr