python - Memory leak using scipy.eigs -

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i have set of large sparse matrices i'm trying find eigenvector corresponding eigenvalue 0 of. structure of underlying problem know solution must exist , 0 largest eigenvalue.

to solve problem use scipy.sparse.linalg.eigs arguments:

val, rho = eigs(l, k=1, = 'lm', v0=init, sigma=-0.001)

where l matrix. call multiple times different matrices. @ first having severe problem memory usage increases call function more , more times. seems eigs doesn't free memory should. solved calling gc.collect() each time use eigs.

but worry internally memory isn't being freed, naively expect using arnoldi shouldn't use more memory algorithm progresses, should storing matrix , current set of lanczos vectors, find memory usage increases while code still inside eigs function.

any ideas?


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