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  "body": "\n## Statement\n\nLet $n \\ge 1$ and let $A$ and $B$ be $n \\times n$ complex [[positive semidefinite matrices|01MMVW8CKC]].\nThen their [[Hadamard product|SQT6QCEXZ8]] $A \\circ B$, the matrix with entries $(A \\circ B)_{ij} = a_{ij} b_{ij}$, is positive semidefinite.\n\n## Remarks\n\nBy the [[spectral theorem for Hermitian matrices|0DGZBQWZWN]], $B = \\sum_k v_k v_k^*$ for some vectors $v_k \\in \\C^n$.\nFor one vector $v$, $A \\circ (v v^*) = D A D^*$ with $D = \\operatorname{diag}(v_1, \\ldots, v_n)$, which is positive semidefinite.\nSo $A \\circ B = \\sum_k D_k A D_k^*$ is a sum of positive semidefinite matrices.\nExample: $A = \\begin{pmatrix} 2 \u0026 1 \\\\ 1 \u0026 2 \\end{pmatrix}$ and $B = \\begin{pmatrix} 1 \u0026 -1 \\\\ -1 \u0026 2 \\end{pmatrix}$ are positive definite, and $A \\circ B = \\begin{pmatrix} 2 \u0026 -1 \\\\ -1 \u0026 4 \\end{pmatrix}$ has trace $6$ and determinant $7$, so it is positive definite.\nIf $A$ and $B$ are both [[positive definite|0KX66CZMBB]], then so is $A \\circ B$.\nThe hypothesis on $B$ is needed: with $A = I_2$ and $B = \\operatorname{diag}(1, -1)$, $A \\circ B = B$ is not positive semidefinite.\n\n## Explanations\n\n### L1 human\n\nMultiply two matrices entry by entry, not by the usual row-times-column rule.\nIf both matrices are positive semidefinite, the result is again positive semidefinite.\nFor the usual product this would fail, since $AB$ need not even be Hermitian.\n\n### L2 human\n\nPositive semidefinite matrices are [[Gram matrices|00SPJSDT3Z]] $a_{ij} = \\ip{x_i}{x_j}$, and the entrywise product of two Gram matrices is the Gram matrix of the tensor products $x_i \\otimes y_i$; this gives a second proof.\nThe theorem shows that the entrywise power $(a_{ij}^k)$ and the entrywise exponential $(e^{a_{ij}})$ of a positive semidefinite matrix are positive semidefinite.\nThis is why sums, products and exponentials of positive definite kernels are again positive definite kernels in machine learning.\n",
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