Basic Examples (2) 

Compute a Kronecker product approximation of a small matrix:

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Form the approximant to the original matrix:

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Using two terms gives a much better approximant:

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The Frobenius norm of the remainder matrix is on the order of machine precision:

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Define a 10×12 matrix:

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Express the matrix as a sum of three Kronecker products of matrices of dimensions 5×3 and 2×4:

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Check the difference between the original matrix and the sum of the Kronecker products:

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Scope (3) 

A 10×12 matrix with approximate numerical values:

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Express the matrix as a sum of up to seven Kronecker products of matrices of dimensions 2×3 and 5×4:

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Check the difference between the original matrix and the sum of the Kronecker products:

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An arbitrary-precision 10×12 matrix:

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Express the matrix as a sum of up to seven Kronecker products of matrices of dimensions 2×3 and 5×4:

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Check the difference between the original matrix and the sum of the Kronecker products:

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Decompose a random complex-valued 10×12 matrix into a sum of two Kronecker products of matrices of dimensions 2×3 and 5×4:

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Possible Issues (4) 

NearestKroneckerProductSum is left unevaluated for symbolic input:

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NearestKroneckerProductSum is left unevaluated for exact input:

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Numericize the input to get a result:

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NearestKroneckerProductSum is left unevaluated if the specified dimensions are incommensurate:

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The decomposition of a matrix into a sum of Kronecker products is not unique:

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