regularized_lsq_with_qr(m, n, R, QTb, perm, diag, copy_R=True)
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.
Initial shape of A.
Upper triangular matrix from QR decomposition of A.
First n components of Q^T b.
Array defining column permutation of A, such that ith column of P is perm[i]-th column of identity matrix.
Array containing diagonal elements of D.
Found least-squares solution.
Solve regularized least squares using information from QR-decomposition.
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them