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  2. Matrix norm - Wikipedia

    en.wikipedia.org/wiki/Matrix_norm

    The Frobenius norm is an extension of the Euclidean norm to and comes from the Frobenius inner product on the space of all matrices. The Frobenius norm is sub-multiplicative and is very useful for numerical linear algebra. The sub-multiplicativity of Frobenius norm can be proved using Cauchy–Schwarz inequality.

  3. Perron–Frobenius theorem - Wikipedia

    en.wikipedia.org/wiki/Perron–Frobenius_theorem

    Let = be an positive matrix: > for ,.Then the following statements hold. There is a positive real number r, called the Perron root or the Perron–Frobenius eigenvalue (also called the leading eigenvalue, principal eigenvalue or dominant eigenvalue), such that r is an eigenvalue of A and any other eigenvalue λ (possibly complex) in absolute value is strictly smaller than r, |λ| < r.

  4. Cauchy–Schwarz inequality - Wikipedia

    en.wikipedia.org/wiki/Cauchy–Schwarz_inequality

    where , is the inner product.Examples of inner products include the real and complex dot product; see the examples in inner product.Every inner product gives rise to a Euclidean norm, called the canonical or induced norm, where the norm of a vector is denoted and defined by ‖ ‖:= , , where , is always a non-negative real number (even if the inner product is complex-valued).

  5. Trace (linear algebra) - Wikipedia

    en.wikipedia.org/wiki/Trace_(linear_algebra)

    The norm derived from this inner product is called the Frobenius norm, and it satisfies a submultiplicative property, as can be proven with the Cauchy–Schwarz inequality: [⁡ ()] ⁡ ⁡ , if A and B are real positive semi-definite matrices of the same size.

  6. Frobenius inner product - Wikipedia

    en.wikipedia.org/wiki/Frobenius_inner_product

    In mathematics, the Frobenius inner product is a binary operation that takes two matrices and returns a scalar.It is often denoted , .The operation is a component-wise inner product of two matrices as though they are vectors, and satisfies the axioms for an inner product.

  7. Dual norm - Wikipedia

    en.wikipedia.org/wiki/Dual_norm

    The Frobenius norm defined by ‖ ‖ = = = | | = ⁡ = = {,} is self-dual, i.e., its dual norm is ‖ ‖ ′ = ‖ ‖.. The spectral norm, a special case of the induced norm when =, is defined by the maximum singular values of a matrix, that is, ‖ ‖ = (), has the nuclear norm as its dual norm, which is defined by ‖ ‖ ′ = (), for any matrix where () denote the singular values ...

  8. Specht's theorem - Wikipedia

    en.wikipedia.org/wiki/Specht's_theorem

    If A and B are unitarily equivalent, then tr AA* = tr BB*, where tr denotes the trace (in other words, the Frobenius norm is a unitary invariant). This follows from the cyclic invariance of the trace: if B = U *AU, then tr BB* = tr U *AUU *A*U = tr AUU *A*UU * = tr AA*, where the second equality is cyclic invariance. [3]

  9. Talk:Matrix norm - Wikipedia

    en.wikipedia.org/wiki/Talk:Matrix_norm

    The section on the Frobenius norm contains this sentence: "The Frobenius norm is sub-multiplicative and is very useful for numerical linear algebra. The sub-multiplicativity of Frobenius norm can be proved using Cauchy–Schwarz inequality."