Memory Error In Numpy Svd

I am performing numpy svd U, S, V = np.linalg.svd(A) shape of A is : (10000, 10000) Due to the large size, it gives me memory error : U, S, V = np.linalg.svd(A, full_matrices=

Solution 1:

Some small tips: Close everything else that is open on your computer. Remove all unnecessary memory hogging things in your program by setting the variables you don't need anymore to None. Say you used a big dict D for some computations earlier but don't need it anymore set D = None. Try initializing your numpy arrays with dtype=np.int32 or dtype=np.float32 to lower memory requirements.

Depending on what you need the SVD for you can also have a look at the scikit-learn package for python, they have support for many decomposition methods such as PCA and SVD together with sparse matrix support.

Solution 2:

There is a light implementation of SVD which is called thin-SVD. It is used when your base matrix is approximately low-rank. Considering the dimensions of your matrix, it is highly likely that it is a low-rank matrix since almost all big matrices are low rank according to a paper entitled, "Why are Big Data Matrices Approximately Low Rank?" hence, thin-SVD might solve this problem by not calculating all singular values and their singular vectors. Rather it aims at finding the highest singular values.

To find the corresponding implemetation you can search for: sklearn.decomposition.TruncatedSVD¶