The classical Kharitonov theorem on interval stability cannot be carried over from polynomials to arbitrary entire functions. In this paper we identify a class of entire functions for which the desired generalization of the Kharitonov theorem can be proven. The class is wide enough to include classes quasi-polynomials occurring in the study of retarded systems with time delays, and some classes of entire functions that could be useful in studying systems with distributed delays. We also derive results for matrix polynomials and matrix entire functions.
Several classes of structured matrices (e.g., the Hadamard-Sylvester matrices and the pseudo-noise matrices) are used in the design of error-correcting codes. In particular, the columns of matrices belonging to the above two matrix classes are often used as codewords. In this paper we show that the two above classes essentially coincide: the pseudo-noise matrices can be obtained from the Hadamard-Sylvester matrices by means of the row/column permutations.
The paper provides a fully self-contained derivation of fast
algorithms to compute discrete Cosine and Sine transforms I - II
based on the concept of the comrade matrix. The comrade matrices
associated with different versions of the transforms differ in
only a few boundary elements; hence, in each case algorithms can
be derived in a unified manner.
KEYWORDS: Matrices, Condition numbers, Mathematics, Signal processing, Algorithms, Control systems, Platinum, Computing systems, Image information entropy, Linear algebra
Let P be a symmetric positive definite Pick matrix of order n. The following facts will be proven here: (1) P is the Gram matrix of a set of rational functions, with respect to an inner product defined in terms of a 'generating function' associated to P; (2) Its condition number is lower-bounded by a function growing exponentially in n. (3) P can be effectively preconditioned by the Pick matrix generated by the same nodes and a constant function.
This paper gives displacement structure algorithms for the factorization positive definite and indefinite Hankel and Hankel- like matrices. The positive definite algorithm uses orthogonal symplectic transformations in place of the (Sigma) -orthogonal transformations used in Toeplitz algorithms. The indefinite algorithm uses a look-ahead step and is based on the observation that displacement structure algorithms for Hankel factorization have a natural and simple block generalization. Both algorithms can be applied to Hankel-like matrices of arbitrary displacement rank.
The classical Caratheodory-Fejer and Nevanlinna-Pick interpolation problems have a long and distinguished history, appearing in a variety of applications in mathematics and electrical engineering. It is well-known that these problems can be solved in O(n2) operations, where n is the overall multiplicity of interpolation points. In this paper we suggest a superfast algorithm for solving the more general confluent tangential interpolation problem. The cost of the new algorithm varies from O(n log2 n) to O(n log3 n), depending on the multiplicity pattern of the interpolation points. The new algorithm can be used to factorize, invert, and solve a linear system of equations with confluent- Cauchy-like matrices. This class of matrices includes Hankel-like (i.e., permuted Toeplitz-like), Vandermonde-like and Cauchy-like matrices as special cases. An important ingredient of the proposed method is a new fast algorithm to compute a product of a confluent- Cauchy-like matrix by a vector.
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