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Update dpnp.linalg.matrix_power() implementation #1748
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View rendered docs @ https://intelpython.github.io/dpnp/index.html |
This table shows the performance of Results for @vtavana do you know about this or is it a regression?
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There is always some sort of variation in timing on Iris Xe. However, for this case
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* Add an implementation of dpnp.linalg.matrix_power * Update cupy tests for matrix_power * Add dpnp tests for matrix_power * Use add no_bool in tests to avoid singilar input matrix * Address remarks * Improve performance for _stacked_identity functions * Add TestMatrixPowerBatched to cupy tests * Update dpnp tests for matrix_power * Efficient use of binary decomposition --------- Co-authored-by: Anton <100830759+antonwolfy@users.noreply.github.com> e44469c
This PR updates a
dpnp.linalg.matrix_power()
function to raise an input square matrix to the powern
usingdpnp.linalg.inv()
anddpnp.matmul()
The changes are related to support of all types.