Solution of the linear system a x = b, where a and b are general multidimensional arrays. The structure and dimension names of the result are inferred from the arguments.

Solution of the homogeneous linear system a x = 0, where a is a general multidimensional array.

If the system is overconstrained we may provide the theoretical rank to get a MSE solution.

A simpler way to use solveHomog, which returns just one solution. If the system is overconstrained it returns the MSE solution.

solveHomog1 for single letter index names.

Solution of the linear system a x = b, where a and b are general multidimensional arrays, with homogeneous equality along a given index.

optimization parameters for alternating least squares

nMax = 20, epsilon = 1E-3, delta = 1, post = id, postk = const id, presys = id

Solution of a multilinear system a x y z ... = b based on alternating least squares.

Solution of the homogeneous multilinear system a x y z ... = 0 based on alternating least squares.

Solution of a multilinear system a x y z ... = b, with a homogeneous index, based on alternating least squares.

Given two arrays a (source) and b (target), we try to compute linear transformations x,y,z,... for each dimension, such that product [a,x,y,z,...] == b. (We can use eqnorm for post processing, or id.)

Homogeneous factorized system. Given an array a, given as a list of factors as, and a list of pairs of indices ["pi","qj", "rk", etc.], we try to compute linear transformations x!"pi", y!"pi", z!"rk", etc. such that product [a,x,y,z,...] == 0.

post processing function that modifies a list of tensors so that they have equal frobenius norm

debugging function (e.g. for presys), which shows rows, columns and rank of the coefficient matrix of a linear system.