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  2. Perron–Frobenius theorem - Wikipedia

    en.wikipedia.org/wiki/PerronFrobenius_theorem

    The Perron–Frobenius theorem describes the properties of the leading eigenvalue and of the corresponding eigenvectors when A is a non-negative real square matrix. Early results were due to Oskar Perron ( 1907 ) and concerned positive matrices.

  3. Perron number - Wikipedia

    en.wikipedia.org/wiki/Perron_number

    Perron numbers are named after Oskar Perron; the Perron–Frobenius theorem asserts that, for a real square matrix with positive algebraic entries whose largest eigenvalue is greater than one, this eigenvalue is a Perron number. As a closely related case, the Perron number of a graph is defined to be the spectral radius of its adjacency matrix.

  4. Transfer operator - Wikipedia

    en.wikipedia.org/wiki/Transfer_operator

    The left-adjoint of the Perron–Frobenius operator is the Koopman operator or composition operator. The general setting is provided by the Borel functional calculus . As a general rule, the transfer operator can usually be interpreted as a (left-) shift operator acting on a shift space .

  5. Frobenius theorem - Wikipedia

    en.wikipedia.org/wiki/Frobenius_theorem

    Frobenius reciprocity theorem in group representation theory describing the reciprocity relation between restricted and induced representations on a subgroup Perron–Frobenius theorem in matrix theory concerning the eigenvalues and eigenvectors of a matrix with positive real coefficients

  6. Pushforward measure - Wikipedia

    en.wikipedia.org/wiki/Pushforward_measure

    In general, any measurable function can be pushed forward. The push-forward then becomes a linear operator, known as the transfer operator or FrobeniusPerron operator.In finite spaces this operator typically satisfies the requirements of the FrobeniusPerron theorem, and the maximal eigenvalue of the operator corresponds to the invariant measure.

  7. Stochastic matrix - Wikipedia

    en.wikipedia.org/wiki/Stochastic_matrix

    On the other hand, the Perron–Frobenius theorem also ensures that every irreducible stochastic matrix has such a stationary vector, and that the largest absolute value of an eigenvalue is always 1. However, this theorem cannot be applied directly to such matrices because they need not be irreducible. In general, there may be several such vectors.

  8. Adjacency matrix - Wikipedia

    en.wikipedia.org/wiki/Adjacency_matrix

    This can be seen as result of the Perron–Frobenius theorem, but it can be proved easily. Let v be one eigenvector associated to λ 1 {\displaystyle \lambda _{1}} and x the entry in which v has maximum absolute value.

  9. Frobenius theorem (differential topology) - Wikipedia

    en.wikipedia.org/wiki/Frobenius_theorem...

    The Frobenius theorem can be restated more economically in modern language. Frobenius' original version of the theorem was stated in terms of Pfaffian systems, which today can be translated into the language of differential forms. An alternative formulation, which is somewhat more intuitive, uses vector fields.