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Sage Reference Manual: Matrices and Spaces of Matrices - Mirrors

Sage Reference Manual: Matrices and Spaces of Matrices - Mirrors

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<strong>Sage</strong> <strong>Reference</strong> <strong>Manual</strong>: <strong>Matrices</strong> <strong>and</strong> <strong>Spaces</strong> <strong>of</strong> <strong>Matrices</strong>, Release 6.1.1<br />

(a2, Vector space <strong>of</strong> degree 7 <strong>and</strong> dimension 2 over Number Field in a2 with defining polynomi<br />

[ 0 1 0 1 -1 1 -1]<br />

[ 0 0 1 0 -1 2 -1], 2),<br />

(-1.414213562373095, Vector space <strong>of</strong> degree 7 <strong>and</strong> dimension 2 over Algebraic Field<br />

User basis matrix:<br />

[ 0 1 0 -1 0.414213562<br />

[ 0 0 1 0<br />

(1.414213562373095, Vector space <strong>of</strong> degree 7 <strong>and</strong> dimension 2 over Algebraic Field<br />

User basis matrix:<br />

[ 0 1 0 -1 -2.414<br />

[ 0 0 1 0<br />

]<br />

sage: A.eigenspaces_left(format=’galois’, algebraic_multiplicity=True)<br />

[<br />

(3, Vector space <strong>of</strong> degree 7 <strong>and</strong> dimension 1 over Rational Field<br />

User basis matrix:<br />

[ 1 0 1/7 0 -1/7 0 -2/7], 1),<br />

(-2, Vector space <strong>of</strong> degree 7 <strong>and</strong> dimension 2 over Rational Field<br />

User basis matrix:<br />

[ 0 1 0 1 -1 1 -1]<br />

[ 0 0 1 0 -1 2 -1], 2),<br />

User basis matrix:<br />

[ 0 1 0 -1 -a2 - 1 1 -1]<br />

[ 0 0 1 0 -1 0 -a2 + 1], 2)<br />

]<br />

Next we compute the left eigenspaces over the finite field <strong>of</strong> order 11.<br />

sage: A = ModularSymbols(43, base_ring=GF(11), sign=1).T(2).matrix(); A<br />

[ 3 9 0 0]<br />

[ 0 9 0 1]<br />

[ 0 10 9 2]<br />

[ 0 9 0 2]<br />

sage: A.base_ring()<br />

Finite Field <strong>of</strong> size 11<br />

sage: A.charpoly()<br />

x^4 + 10*x^3 + 3*x^2 + 2*x + 1<br />

sage: A.eigenspaces_left(format=’galois’, var = ’beta’)<br />

[<br />

(9, Vector space <strong>of</strong> degree 4 <strong>and</strong> dimension 1 over Finite Field <strong>of</strong> size 11<br />

User basis matrix:<br />

[0 0 1 5]),<br />

(3, Vector space <strong>of</strong> degree 4 <strong>and</strong> dimension 1 over Finite Field <strong>of</strong> size 11<br />

User basis matrix:<br />

[1 6 0 6]),<br />

(beta2, Vector space <strong>of</strong> degree 4 <strong>and</strong> dimension 1 over Univariate Quotient Polynomial Ring in<br />

User basis matrix:<br />

[ 0 1 0 5*beta2 + 10])<br />

]<br />

This method is only applicable to exact matrices. The “eigenmatrix” routines for matrices with doubleprecision<br />

floating-point entries (RDF, CDF) are the best alternative. (Since some platforms return eigenvectors<br />

that are the negatives <strong>of</strong> those given here, this one example is not tested here.) There are also<br />

“eigenmatrix” routines for matrices with symbolic entries.<br />

sage: A = matrix(QQ, 3, 3, range(9))<br />

sage: A.change_ring(RR).eigenspaces_left()<br />

Traceback (most recent call last):<br />

...<br />

160 Chapter 7. Base class for matrices, part 2

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