Quickly And Efficiently Calculating An Eigenvector For Known Eigenvalue

Short version of my question: What would be the optimal way of calculating an eigenvector for a matrix A, if we already know the eigenvalue belonging to the eigenvector? Longer exp

Solution 1:

Here's one approach using Matlab:

1. Let x denote the (row) left eigenvector associated to eigenvalue 1. It satisfies the system of linear equations (or matrix equation) xA = x, or x(A−I)=0.
2. To avoid the all-zeros solution to that system of equations, remove the first equation and arbitrarily set the first entry of x to 1 in the remaining equations.
3. Solve those remaining equations (with x₁ = 1) to obtain the other entries of x.

Example using Matlab:

>> A = [.6 .1 .3
        .2 .7 .1
        .5 .1 .4]; %// example stochastic matrix
>> x = [1, -A(1, 2:end)/(A(2:end, 2:end)-eye(size(A,1)-1))]
x =
   1.000000000000000   0.529411764705882   0.588235294117647
>> x*A %// check
ans =
   1.000000000000000   0.529411764705882   0.588235294117647

Note that the code -A(1, 2:end)/(A(2:end, 2:end)-eye(size(A,1)-1)) is step 3.

In your formulation you define x to be a (column) right eigenvector of A (such that Ax = x). This is just x.' from the above code:

>> x = x.'
x =
   1.000000000000000
   0.529411764705882
   0.588235294117647
>> A.'*x %// check
ans =
   1.000000000000000
   0.529411764705882
   0.588235294117647

You can of course normalize the eigenvector to sum 1:

>> x = x/sum(x)
x =
   0.472222222222222
   0.250000000000000
   0.277777777777778
>> A.'*x %'// check
ans =
   0.472222222222222
   0.250000000000000
   0.277777777777778

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