Generalized Householder Transformations

The Householder transformation, allowing a rewrite of probabilities into expectations of dichotomic observables, is generalized in terms of its spectral decomposition. Dichotomy is modulated by allowing more than one negative eigenvalues, or by abandoning it altogether, yielding generalized operator valued arguments for contextuality. We also discuss a form of state-dependent contextuality by variation of the functional relations of the operators; in particular, by additivity.

HOUSEHOLDER TRANSFORMATION IN THE INVERSE PROBLEM FOR A COMPLEX VIBRONIC ANALOGUE OF THE FERMI RESONANCE

In the inverse problem for a complex vibronic analogue of the Fermi resonance, the matrix elements of the electron-vibration interaction should be obtained from experimental data, energies Ek and intensities Ik (k = 1, 2, …, n; n ≥ 3), a “conglomerate” of lines in the spectrum. This problem in the direct-coupling model, where the Hamiltonian HDIR is specified by the energies of the “dark” states Ai and the matrix elements of their coupling with the “bright” state Bi (i = 1, 2, …, n –1), was solved by the author on the basisof algebraic methods. It is shown that the Hamiltonian HDW of the doorway-coupling model, in which the “bright” state has “interaction” with only single distinguished |DW> state, can be obtained from the Hamiltonian HDIR using the Householder triangularization method, namely, by the similarity transformation HDW = PHDIRP, where P is the reflection matrix which is constructed from the Bi values. The expressions for main elements of the doorway model, namely, the energy of the |DW> state and the matrix element of its coupling with the "bright" state, are obtained. For pyrazine and acetylene molecules, the matrix elements of the Hamiltonian HDW are calculated using the data of the electronic-vibrational-rotational spectra.

ROTATION MATRIX SAMPLING SCHEME FOR MULTIDIMENSIONAL PROBABILITY DISTRIBUTION TRANSFER

This paper address the problem of rotation matrix sampling used for multidimensional probability distribution transfer. The distribution transfer has many applications in remote sensing and image processing such as color adjustment for image mosaicing, image classification, and change detection. The sampling begins with generating a set of random orthogonal matrix samples by Householder transformation technique. The advantage of using the Householder transformation for generating the set of orthogonal matrices is the uniform distribution of the orthogonal matrix samples. The obtained orthogonal matrices are then converted to proper rotation matrices. The performance of using the proposed rotation matrix sampling scheme was tested against the uniform rotation angle sampling. The applications of the proposed method were also demonstrated using two applications i.e., image to image probability distribution transfer and data Gaussianization.

ROTATION MATRIX SAMPLING SCHEME FOR MULTIDIMENSIONAL PROBABILITY DISTRIBUTION TRANSFER

This paper address the problem of rotation matrix sampling used for multidimensional probability distribution transfer. The distribution transfer has many applications in remote sensing and image processing such as color adjustment for image mosaicing, image classification, and change detection. The sampling begins with generating a set of random orthogonal matrix samples by Householder transformation technique. The advantage of using the Householder transformation for generating the set of orthogonal matrices is the uniform distribution of the orthogonal matrix samples. The obtained orthogonal matrices are then converted to proper rotation matrices. The performance of using the proposed rotation matrix sampling scheme was tested against the uniform rotation angle sampling. The applications of the proposed method were also demonstrated using two applications i.e., image to image probability distribution transfer and data Gaussianization.