Let be a ( fixed ) positive definite matrix of size.
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Conversely, every positive semi-definite matrix is the covariance matrix of some multivariate distribution.
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If " M " is a positive definite matrix, this yields an inner product.
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With smooth and bounded coefficients and a positive definite matrix ( a _ { ij } ).
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Interval Finite Element Method require the solution of parameter dependent system of equations ( usually with symmetric positive definite matrix ).
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And the log-concavity of the determinant of a positive definite matrix implies that " D " = 1.
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We need to make " P " a positive semi-definite matrix in order to reformulate a convex function.
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A symmetric matrix and another symmetric and positive-definite matrix can be simultaneously diagonalized, although not necessarily via a similarity transformation.
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*For any real invertible matrix A, the product A ^ { \ mathrm { T } } A is a positive definite matrix.
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Every Hermitian positive-definite matrix ( and thus also every real-valued symmetric positive-definite matrix ) has a unique Cholesky decomposition.
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