About: Polynomial matrix is a research topic. Over the lifetime, 3736 publications have been published within this topic receiving 59047 citations. The topic is also known as: matrix of polynomials & lambda-matrix.
TL;DR: The method of showing density ymlds the result that if P ~ NP then there are members of NP -P that are not polynomml complete is shown, which means there is a strictly ascending sequence with a minimal pair of upper bounds to the sequence.
Abstract: Two notions of polynomml time reduclbihty, denoted here by ~ T e and <.~P, were defined by Cook and Karp, respectively The abstract propertms of these two relatmns on the domain of computable sets are investigated. Both relations prove to be dense and to have minimal pairs. Further , there is a strictly ascending sequence with a minimal pair of upper bounds to the sequence. Our method of showing density ymlds the result that if P ~ NP then there are members of NP -P that are not polynomml complete
TL;DR: In this paper, the existence of non-negative storage functions was shown to be equivalent to the equivalence of dissipativity with existence of a nonnegative storage with constant-coefficient linear PDE.
TL;DR: Several algorithms for affecting decompositions for the class of rational matrices G(p) , i.e., matrices whose entries are ratios of polynomials in p .
Abstract: Many problems in electrical engineering, such as the synthesis of linear n ports and the detection and filtration of multivariable systems corrupted by stationary additive noise, depend for their successful solution upon the factorization of a matrix-valued function of a complex variable p . This paper presents several algorithms for affecting such decompositions for the class of rational matrices G(p) , i.e., matrices whose entries are ratios of polynomials in p . The methods employed are elementary in nature and center around the Smith canonic form of a polynomial matrix. Several nontrivial examples are worked out in detail to illustrate the theory.
TL;DR: The parallel arithmetic complexities of matrix inversion, solving systems of linear equations, computing determinants and computing the characteristic polynomial of a matrix are shown to have the same growth rate.
Abstract: The parallel arithmetic complexities of matrix inversion, solving systems of linear equations, computing determinants and computing the characteristic polynomial of a matrix are shown to have the same growth rate. Algorithms are given that compute these problems in $O(\log ^2 n)$ steps using a number of processors polynomial in n. (n is the order of the matrix of the problem.)