Support 'nuc' norm in dpnp.linalg.cond - #3084
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antonwolfy wants to merge 3 commits into
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Add the nuclear norm ('nuc') to the set of norms accepted by
dpnp.linalg.cond. The condition number for 'nuc' is already computed
correctly through the inverse-based branch (norm(x, 'nuc') *
norm(inv(x), 'nuc')); this change documents it as a supported value of
p and adds test coverage.
Unlike NumPy, dpnp.linalg.inv raises LinAlgError on singular input, so
cond with 'nuc' raises on singular matrices consistently with the other
inverse-based norms (1, -1, inf, -inf, 'fro'); no special NaN handling
is required.
Update the docstring (parameter set, norm table, Notes, example), add
'nuc' to the TestCond norm list and to the cond sycl-queue/usm-type
propagation tests, and add a CHANGELOG entry.
antonwolfy
requested review from
ndgrigorian and
vlad-perevezentsev
as code owners
October 2, 2026 16:53
Contributor
|
View rendered docs @ https://intelpython.github.io/dpnp/pull/3084/index.html |
Collaborator
|
coverage: 78.622%. remained the same — support-nuc-in-cond into master |
Contributor
|
Array API standard conformance tests for dpnp=0.21.0dev11=np2py314h8d9cdd5_17 ran successfully. |
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This PR adds the nuclear norm (
"nuc") to the set of norms accepted bydpnp.linalg.cond, aligning its supportedpvalues withdpnp.linalg.normand with NumPy.For
"nuc"the condition number is computed through the existing inverse-based branch asnorm(x, "nuc") * norm(inv(x), "nuc"), i.e. the product of the sums of the singular values ofxandinv(x). This path was already functional (dpnp.linalg.normsupports"nuc"for matrices), so no change to the computation itself is required — the result matches NumPy for non-singular, batched, complex, boolean, empty, and strided inputs.Consistent with the other inverse-based norms (
1,-1,inf,-inf,"fro"),dpnp.linalg.condraisesLinAlgErroron singular input whenp="nuc", becausedpnp.linalg.invraises on singular matrices rather than filling the result withinfthe way NumPy does.