Wolfram Function Repository
Instant-use add-on functions for the Wolfram Language
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Create a random sparse symmetric positive definite matrix using the Python package ProbNum
ResourceFunction["RandomSparseSPDMatrixProbNum"][n] creates an n⨯n random sparse symmetric positive definite matrix. | |
ResourceFunction["RandomSparseSPDMatrixProbNum"][session,…] uses the specified running ExternalSessionObject. |
"CholeskyFactorDensity" | 0.1 | density of the off-diagonal entries of the Cholesky factor |
“CholeskyFactorRange" | {0.1,1.} | the min and max values of the off-diagonal elements of the Cholesky factor |
RandomSeeding | Automatic | seeding of the pseudorandom generator |
Construct a random symmetric positive definite matrix:
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Show the matrix:
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Verify that the matrix is symmetric and positive definite:
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Construct a matrix:
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Use a the specified Python session:
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Close the session:
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Use the option "CholeskyFactorDensity" to construct a dense matrix:
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Create a matrix with larger numerical values using the "CholeskyFactorRange" option:
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Ensure reproducibility of the results:
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Use the specified random number generator from the Python package numpy for seeding:
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A random sparse symmetric positive definite matrix can be constructed with the resource function ProbNumObject:
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Construct the same matrix with RandomSparseSPDMatrixProbNum:
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This work is licensed under a Creative Commons Attribution 4.0 International License