969 research outputs found
Computing the Rank and a Small Nullspace Basis of a Polynomial Matrix
We reduce the problem of computing the rank and a nullspace basis of a
univariate polynomial matrix to polynomial matrix multiplication. For an input
n x n matrix of degree d over a field K we give a rank and nullspace algorithm
using about the same number of operations as for multiplying two matrices of
dimension n and degree d. If the latter multiplication is done in
MM(n,d)=softO(n^omega d) operations, with omega the exponent of matrix
multiplication over K, then the algorithm uses softO(MM(n,d)) operations in K.
The softO notation indicates some missing logarithmic factors. The method is
randomized with Las Vegas certification. We achieve our results in part through
a combination of matrix Hensel high-order lifting and matrix minimal fraction
reconstruction, and through the computation of minimal or small degree vectors
in the nullspace seen as a K[x]-moduleComment: Research Report LIP RR2005-03, January 200
Asymptotically fast polynomial matrix algorithms for multivariable systems
We present the asymptotically fastest known algorithms for some basic
problems on univariate polynomial matrices: rank, nullspace, determinant,
generic inverse, reduced form. We show that they essentially can be reduced to
two computer algebra techniques, minimal basis computations and matrix fraction
expansion/reconstruction, and to polynomial matrix multiplication. Such
reductions eventually imply that all these problems can be solved in about the
same amount of time as polynomial matrix multiplication
Algorithms for zero-dimensional ideals using linear recurrent sequences
Inspired by Faug\`ere and Mou's sparse FGLM algorithm, we show how using
linear recurrent multi-dimensional sequences can allow one to perform
operations such as the primary decomposition of an ideal, by computing the
annihilator of one or several such sequences.Comment: LNCS, Computer Algebra in Scientific Computing CASC 201
Computational linear algebra over finite fields
We present here algorithms for efficient computation of linear algebra
problems over finite fields
General computational approach for optimal fault detection
We propose a new computational approach to solve the optimal fault detection
problem in the most general setting. The proposed procedure is free of any technical assumptions
and is applicable to both proper and non-proper systems. This procedure forms the basis of
an integrated numerically reliable state-space algorithm, which relies on powerful descriptor
systems techniques to solve the underlying computational subproblems. The new algorithm has
been implemented into a Fault Detection Toolbox for Matlab
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