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The operator is said to be positive-definite, and written >, if , >, for all {}. [ 1 ] Many authors define a positive operator A {\displaystyle A} to be a self-adjoint (or at least symmetric) non-negative operator.
Since the power-series coefficients of the exponential are well known, and higher-order derivatives of the monomial x n can be written down explicitly, this differential-operator representation gives rise to a concrete formula for the coefficients of H n that can be used to quickly compute these polynomials.
If A is Hermitian and Ax, x ≥ 0 for every x, then A is called 'nonnegative', written A ≥ 0; if equality holds only when x = 0, then A is called 'positive'. The set of self adjoint operators admits a partial order, in which A ≥ B if A − B ≥ 0. If A has the form B*B for some B, then A is nonnegative; if B is invertible, then A is positive.
This is not true in general for Kraus operators obtained from square root factorizations. (Positive semidefinite matrices do not generally have a unique square-root factorizations.) If two sets of Kraus operators {A i} 1 nm and {B i} 1 nm represent the same completely positive map Φ, then there exists a unitary operator matrix
Normal operators are important because the spectral theorem holds for them. The class of normal operators is well understood. Examples of normal operators are unitary operators: N* = N −1; Hermitian operators (i.e., self-adjoint operators): N* = N; skew-Hermitian operators: N* = −N; positive operators: N = MM* for some M (so N is self-adjoint).
The operators must yield real eigenvalues, since they are values which may come up as the result of the experiment. Mathematically this means the operators must be Hermitian. [1] The probability of each eigenvalue is related to the projection of the physical state on the subspace related to that eigenvalue.
Positive maps are monotone, i.e. () for all self-adjoint elements ,. Since ‖ ‖ ‖ ‖ for all self-adjoint elements , every positive map is automatically continuous with respect to the C*-norms and its operator norm equals ‖ ‖.
The Gram matrix is symmetric in the case the inner product is real-valued; it is Hermitian in the general, complex case by definition of an inner product. The Gram matrix is positive semidefinite, and every positive semidefinite matrix is the Gramian matrix for some set of vectors. The fact that the Gramian matrix is positive-semidefinite can ...