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Least-Squares Approximation by Elements from Matrix Orbits Achieved by Gradient Flows on Compact Lie Groups
Let denote the orbit of a complex or real matrix under a certain
equivalence relation such as unitary similarity, unitary equivalence, unitary
congruences etc. Efficient gradient-flow algorithms are constructed to
determine the best approximation of a given matrix by the sum of matrices
in in the sense of finding the Euclidean least-squares
distance
Connections of the results to different pure and applied areas are discussed
Quantization of multidimensional cat maps
In this work we study cat maps with many degrees of freedom. Classical cat
maps are classified using the Cayley parametrization of symplectic matrices and
the closely associated center and chord generating functions. Particular
attention is dedicated to loxodromic behavior, which is a new feature of
two-dimensional maps. The maps are then quantized using a recently developed
Weyl representation on the torus and the general condition on the Floquet
angles is derived for a particular map to be quantizable. The semiclassical
approximation is exact, regardless of the dimensionality or of the nature of
the fixed points.Comment: 33 pages, latex, 6 figures, Submitted to Nonlinearit
Least-Squares Approximation by Elements from Matrix Orbits Achieved by Gradient Flows on Compact Lie Groups
Let denote the orbit of a complex or real matrix under a certain
equivalence relation such as unitary similarity, unitary equivalence, unitary
congruences etc. Efficient gradient-flow algorithms are constructed to
determine the best approximation of a given matrix by the sum of matrices
in in the sense of finding the Euclidean least-squares
distance
Connections of the results to different pure and applied areas are discussed
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