How to find linearly independent rows from a matri

问题:

How to identify the linearly independent rows from a matrix? For instance,

The 4th rows is independent.

回答1:

First, your 3rd row is linearly dependent with 1t and 2nd row. However, your 1st and 4th column are linearly dependent.

Two methods you could use:

Eigenvalue

If one eigenvalue of the matrix is zero, its corresponding eigenvector is linearly dependent. The documentation eig stats the eigenvalues is not necessarily ordered. Don't really know if the eigenvalue in the resulting array is just sorted or have some random order. However, assuming the eigenvalues correspond to your row vectors, a method would be:

import numpy as np

matrix = np.array(
    [
        [0, 1 ,0 ,0],
        [0, 0, 1, 0],
        [0, 1, 1, 0],
        [1, 0, 0, 1]
    ])

lambdas, V =  np.linalg.eig(matrix.T)
# The linearly dependent row vectors 
print matrix[lambdas == 0,:]

Cauchy-Schwarz inequality

To test linear dependence of vectors and figure out which ones, you could use the Cauchy-Schwarz inequality. Basically, if the inner product of the vectors is equal to the product of the norm of the vectors, the vectors are linearly dependent. Here is an example for the columns:

import numpy as np

matrix = np.array(
    [
        [0, 1 ,0 ,0],
        [0, 0, 1, 0],
        [0, 1, 1, 0],
        [1, 0, 0, 1]
    ])

print np.linalg.det(matrix)

for i in range(matrix.shape[0]):
    for j in range(matrix.shape[0]):
        if i != j:
            inner_product = np.inner(
                matrix[:,i],
                matrix[:,j]
            )
            norm_i = np.linalg.norm(matrix[:,i])
            norm_j = np.linalg.norm(matrix[:,j])

            print 'I: ', matrix[:,i]
            print 'J: ', matrix[:,j]
            print 'Prod: ', inner_product
            print 'Norm i: ', norm_i
            print 'Norm j: ', norm_j
            if np.abs(inner_product - norm_j * norm_i) < 1E-5:
                print 'Dependent'
            else:
                print 'Independent'

To test the rows is a similar approach.

Then you could extend this to test all combinations of vectors, but I imagine this solution scale badly with size.

回答2:

With sympy you can find the linear independant rows using: sympy.Matrix.rref:

>>> import sympy 
>>> import numpy as np
>>> mat = np.array([[0,1,0,0],[0,0,1,0],[0,1,1,0],[1,0,0,1]])  # your matrix
>>> _, inds = sympy.Matrix(mat).T.rref()   # to check the rows you need to transpose!
>>> inds
[0, 1, 3]

Which basically tells you the rows 0, 1 and 3 are linear independant while row 2 isn't (it's a linear combination of row 0 and 1).

Then you could remove these rows with slicing:

>>> mat[inds]
array([[0, 1, 0, 0],
       [0, 0, 1, 0],
       [1, 0, 0, 1]])

This also works well for rectangular (not only for quadratic) matrices.

回答3:

I edited the code for Cauchy-Schwartz inequality which scales better with dimension: the inputs are the matrix and its dimension, while the output is a new rectangular matrix which contains along its rows the linearly independent columns of the starting matrix. This works in the assumption that the first column in never null, but can be readily generalized in order to implement this case too. Another thing that I observed is that 1e-5 seems to be a "sloppy" threshold, since some particular pathologic vectors were found to be linearly dependent in that case: 1e-4 doesn't give me the same problems. I hope this could be of some help: it was pretty difficult for me to find a really working routine to extract li vectors, and so I'm willing to share mine. If you find some bug, please report them!!

from numpy import dot, norm, zeros
from numpy.linalg import matrix_rank

def find_li_vectors(dim, R):

    r = matrix_rank(R) 
    index = zeros( r ) #this will save the positions of the li columns in the matrix
    counter = 0
    index[0] = 0 #without loss of generality we pick the first column as linearly independent
    j = 0 #therefore the second index is simply 0

    for i in range(R.shape[0]): #loop over the columns
        if i != j: #if the two columns are not the same
            inner_product = dot( R[:,i], R[:,j] ) #compute the scalar product
            norm_i = norm(R[:,i]) #compute norms
            norm_j = norm(R[:,j])

            #inner product and the product of the norms are equal only if the two vectors are parallel
            #therefore we are looking for the ones which exhibit a difference which is bigger than a threshold
            if absolute(inner_product - norm_j * norm_i) > 1e-4:
                counter += 1 #counter is incremented
                index[counter] = i #index is saved
                j = i #j is refreshed
            #do not forget to refresh j: otherwise you would compute only the vectors li with the first column!!

    R_independent = zeros((r, dim))

    i = 0
    #now save everything in a new matrix
    while( i < r ):
        R_independent[i,:] = R[index[i],:] 
        i += 1

    return R_independent

回答4:

I know this was asked a while ago, but here is a very simple solution. Given an array, the following finds a set of linearly independent vectors by progressively adding a vector and testing if the rank has increased:

from numpy.linalg import matrix_rank

def LI_vecs(dim,mat):
    LI=[M[0]]
    for i in range(dim):
        tmp=[]
        for r in LI:
            tmp.append(r)
        tmp.append(M[i])                #set tmp=LI+[M[i]]
        if matrix_rank(tmp)>len(LI):    #test if M[i] is linearly independent from all (row) vectors in LI
            LI.append(M[i])             #note that matrix_rank does not need to take in a square matrix
    return LI                           #return set of linearly independent (row) vectors

#Example
mat=[[1,2,3,4],[4,5,6,7],[5,7,9,11],[2,4,6,8]]
LI_vecs(4,mat)

回答5:

I interpret the problem as finding rows that are linearly independent from other rows. That is equivalent to finding rows that are linearly dependent on other rows.

Gaussian elimination and treat numbers smaller than a threshold as zeros can do that. It is faster than finding eigenvalues of a matrix, testing all combinations of rows with Cauchy-Schwarz inequality, or singular value decomposition.

See: https://math.stackexchange.com/questions/1297437/using-gauss-elimination-to-check-for-linear-dependence

Problem with floating point numbers:

http://numpy-discussion.10968.n7.nabble.com/Reduced-row-echelon-form-td16486.html