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Normal Forms for General Polynomial Matrices Normal Forms for General Polynomial Matrices Bernhard Beckermann a aLaboratoire d'Analyse Num¶erique et d'Optimisation, Universit¶e des Sciences et Technologies de Lille, 59655 Villeneuve d'Ascq Cedex, France. George Labahn b bSymbolic Computation Group, School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada. Gilles Villard c cCnrs-Lip Ecole Normale Sup¶erieure de Lyon, 46, All¶ee d'Italie, 69364 Lyon Cedex 07, France. Abstract We present an algorithm for the computation of a shifted Popov Normal Form of a rectangular polynomial matrix. For speci¯c input shifts, we obtain methods for computing the matrix greatest common divisor of two matrix polynomials (in normal form) or such polynomial normal form computation as the classical Popov form and the Hermite Normal Form. The method is done by embedding the problem of computing shifted forms into one of computing matrix rational approximants. This has the advantage of allowing for fraction-free computations over integral domains such as ZZ [z] or K[z1; : : : ; zn][z]. In the case of rectangular matrix input, the corresponding multipliers for the shifted forms are not unique. We use the concept of minimal matrix approximants to introduce a notion of minimal mutipliers and show how such multipliers are computed by our methods. Key words: Popov Normal Form, Hermite Normal Form, Matrix Gcd, Exact Arithmetic, Fraction-Free Algorithm. Email addresses: [email protected] (Bernhard Beckermann), [email protected] (George Labahn), [email protected] (Gilles Villard). Preprint submitted to Elsevier Science 30 January 2004 1 Introduction Two polynomial matrices A(z) and B(z) in K[z]m£n, K a ¯eld, are column equivalent if there exists a unimodular matrix U(z) 2 K[z]n£n such that A(z) ¢ U(z) = B(z). The matrix U(z) corresponds to a sequence of elemen- tary column operations. There exists a number of normal forms for such an equivalence problem, the best known being the Hermite normal form, initially discovered by Hermite [16] for the domain of integers [22,25]. This is an up- per triangular matrix that has the added constraints that the diagonals have the largest degrees in each row. While a triangular form has many obvious advantages for such operations as solving linear equations it also has certain disadvantages. In particular, the column degrees of a Hermite normal form can increase. For such reasons the Popov normal form [26] of a polynomial matrix can give a better form for many problems. This is a form that speci¯es certain normalizations of matrix leading row coe±cients and which has the property that columns degrees are minimal over all column equivalent matrices. A related problem is the computation of matrix greatest common divisors. For two matrix polynomials A(z) and B(z), both with the same row dimension, a left matrix Gcd is a matrix polynomial C(z) satisfying A(z) = C(z) ¢ A^ (z) and B(z) = C(z) ¢ B^(z) and where A^ (z) and B^(z) have only unimodular left divisors. The Matrix Gcd plays an important role in such areas as linear systems theory [18], minimal partial realizations and other application areas. Normal forms plays two important roles in the Matrix Gcd problem. In the ¯rst place Matrix Gcd's are only unique up to multiplication on the right by unimodular polynomial matrices. In order to specify a single answer one asks that the Gcd be in a speci¯c normal form. In addition, matrix Gcd's are usually computed by converting the rectangular matrix polynomial [A(z); B(z)] into a normal form [0; C(z)] where C(z) is precisely the Matrix Gcd in normal form. Shifted normal forms. The solution of a number of normal form problems involving matrix polynomials { particularly the forms mentioned above and others that we will introduce as examples in the paper { may be uni¯ed by the notion of shifted form [6]. Matrix normal forms such as Hermite or Popov have certain degree structures in their requirements. They are typically computed by reducing degrees in certain rows or columns in such a way that the degree requirements will eventually be met. A shift associated to an input matrix A(z) is a vector ~a of integers that may be seen as weights attributed to the rows of the matrix (see De¯nition 2.3). These weights govern the order in which operations are performed during the algorithm and thus allow { by alternate ways of chosing them { one to use the same process for the computation of di®erent forms including for example the Popov and the Hermite forms. One can illustrate this using the two latter forms as an example. The column 2 degrees of z3 ¡ z2 z3 ¡ 2 z2 ¡ 1 A(z) = 2 3 3 2 3 2 6 z ¡ 2 z + 2 z ¡ 2 z ¡ 3 z + 3 z ¡ 4 7 6 7 4 5 may be reduced by unimodular column transformations to obtain z ¡1 T(z) = 2 3 ; 6 1 z ¡ 1 7 6 7 4 5 the Popov form of A(z). With the shift ~a = [0; ¡1] one will give preference to the degrees { and hence to the elimination { in the second row over those in the ¯rst row. This leads to z2 ¡ z + 1 z H(z) = 2 3 6 0 1 7 6 7 4 5 which is the Hermite normal form of A(z) (see [6, Lemma 2.4] and the de¯- nition in Section 2). Additional examples of the use of shifted normal forms are also included later in this paper. For example, it can happen that certain preferences of a whole set of rows of a rectangular matrix will provide a right inverse computation for such a matrix (cf. Example 2.4). Shifted Popov forms were introduced in [5] as a natural and convenient normal form for describing the properties of Mahler systems. These systems are used as basic building blocks for recursively computing solutions to module bases for matrix rational approximation and matrix rational interpolation problems (see also [4] in the case of matrix Pad¶e systems and section 4.2). Shifts also ap- pear naturally in the context of computing normal forms of matrices over ZZ . Schrijver has shown that integer weights on the rows of a matrix A in ZZ n£n leads to a lattice whose reduced basis gives the Hermite form of A [28, p74]. A similar approach is found in [15, x6] where the powers of a positive integer γ give an appropriate shift. For matrix polynomials we develop a more complete study. Computing shifted forms. The primary aim in this paper is to give a new algorithm for computing a shifted Popov form of an arbitrary rank polynomial matrix. In the case where the input is square and nonsingular or rectangular and of full column rank, an algorithm for determining a shifted Popov form 3 has been given in [6]. We extend the methods from [6] to deal with the general case of singular matrices, in particular for those not having full column rank. The Matrix Gcd problem and coprime matrix rational functions gives two im- portant examples which requires normal forms for arbitrary rank matrices. We refer the reader to Examples 2.5 and 3.2 for Matrix Gcd computations and to Example 3.6 for coprime matrix rational functions. Our new algorithm solves two di±culties. The ¯rst one concerns the fact that { unlike in the full column rank case { the multipliers U(z) are not unique. We will de¯ne and compute minimal multipliers. The second di±culty is the intermediate expression swell with respect to polynomial coe±cients. This will be solved by proposing a fraction-free method. Our methods view the equation A(z) ¢ U(z) = T(z) as a kernel computation U(z) [A(z); ¡I] ¢ 2 3 = 0 (1) T(z) 6 7 4 5 and the normal form problem as one of computing a special shifted form of a basis for the kernel of [A(z); ¡I]. This special form provides both the normal form T(z) along with a unimodular multiplier U(z) having certain minimal degree properties. The minimality of degrees overcomes the problem that the unimodular multiplier is not unique. Starting from (1), the same idea of computing column reduced forms using minimal polynomial basis has been used by Beelen et al. [7]. We generalize the approach by using non-constant shifts and by computing di®erent forms. For various purposes, the shift technique may also be applied to the unimodu- lar multipliers. In addition to the input shift ~a associated to A(z) we introduce a second shift ~b associated to U(z). Speci¯c choices of ~b result in unimodu- lar matrices U(z) having special properties that are useful for nullspace and polynomial system solving problems. For example, two bases for the nullspace of z3 + 4 z2 ¡ 2 z2 + 2 z ¡ 1 z3 + z2 z2 ¡ 1 A(z) = 2 3 z4 + z3 + 4 z2 + 2 z z3 + z2 + z 2 z3 + 2 z2 z2 + z 6 7 4 5 4 are given by z + 1 0 ¡z3 ¡ 2 z2 + 1 z2 ¡ 1 2 3 2 3 2 4 3 2 3 6 ¡z ¡ z ¡z ¡ 1 7 6 z + 2 z ¡ z ¡ 4 z ¡ 2 ¡z + 2 z + 1 7 6 7 0 6 7 U(z) = 6 7 and U (z) = 6 7 : 6 7 6 7 6 ¡1 z + 1 7 6 2 z2 + 4 z + 1 ¡2 z 7 6 7 6 7 6 7 6 7 6 7 6 7 6 2 7 6 7 6 ¡2 ¡ z ¡z + 1 ¡ z 7 6 0 1 7 6 7 6 7 4 5 4 5 These two bases are submatrices of multipliers for shifted Popov forms of A(z) (see the detailed study at Section 3).
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