68.3 ms ± 66.2 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
+100 ms ± 980 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
54.4 ms ± 100 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
+72 ms ± 1.09 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
15.5 µs ± 92.4 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
+20 µs ± 742 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
linalg
-markowitzmarkowitz.modelsmarkowitz.models.boundsmarkowitz.typeslinalg
+markowitzlinalglinalg.choleskymarkowitzmarkowitzlinalglinalg.choleskymarkowitz| - | Cholesky decomposition with numpy |
+
|
+Compute the cholesky decomposition of a covariance matrix |
| - | Extract valid submatrix of a matrix |
+
|
+Construct the valid subset of matrix (correlation) matrix |
Compute the cholesky decomposition of a covariance matrix
Construct the valid subset of matrix (correlation) matrix :param matrix: n x n matrix
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diff --git a/tests/coverage/coverage.json b/tests/coverage/coverage.json
index 44cdf6dc..0243dd51 100644
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diff --git a/tests/coverage/coverage.xml b/tests/coverage/coverage.xml
index 52c67e72..06ffb9ed 100644
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