moved tests for eigenvectors to a separate file

Author: Anton Leykin

M2/Macaulay2/tests/normal/000-core.m2

@@ -1389,57 +1389,6 @@ assert ( genera R == {1,2} )
 assert ( eulers R == {0,3} )
 
 
---
-M = matrix{{1.0,1.0},{0.0,1.0}}
-eigenvalues M
-eigenvectors M
-
-M0 = matrix{{1_RR,-1},{1,1}};
-M = M0++M0;
-(D, P) = eigenvectors M
-assert( 1e-10 > norm(M*P - P * diagonalMatrix(D)))
-
-M = matrix{{1.0, 2.0}, {2.0, 1.0}}
-eigenvectors(M, Hermitian=>true)
-
-M = matrix{{1.0, 2.0}, {5.0, 7.0}}
-(eigvals, eigvecs) = eigenvectors M
--- here we use "norm" on vectors!
-assert( 1e-10 > norm ( M * eigvecs_0 - eigvals_0 * eigvecs_0 ) )
-assert( 1e-10 > norm ( M * eigvecs_1 - eigvals_1 * eigvecs_1 ) )
-
-printingPrecision = 2
-
-m = map(CC^10, CC^10, (i,j) -> i^2 + j^3*ii)
-(eigvals, eigvecs) = eigenvectors m
-max (abs \ eigvals) / min (abs \ eigvals)
-scan(#eigvals, i -> assert( 1e-10 > norm ( m * eigvecs_i - eigvals_i * eigvecs_i )))
-
--- some ill-conditioned matrices
-
-m = map(CC^10, CC^10, (i,j) -> (i+1)^(j+1))
-(eigvals, eigvecs) = eigenvectors m
-max (abs \ eigvals) / min (abs \ eigvals)
-apply(#eigvals, i -> norm ( m * eigvecs_i - eigvals_i * eigvecs_i ))
-scan(#eigvals, i -> assert( 1e-4 > norm ( m * eigvecs_i - eigvals_i * eigvecs_i )))
-
-m = map(RR^10, RR^10, (i,j) -> (i+1)^(j+1))
-(eigvals, eigvecs) = eigenvectors m
-max (abs \ eigvals) / min (abs \ eigvals)
-apply(#eigvals, i -> norm ( m * eigvecs_i - eigvals_i * eigvecs_i ))
-scan(#eigvals, i -> assert( 1e-4 > norm ( m * eigvecs_i - eigvals_i * eigvecs_i )))
-
-
-
---
-m = map(CC^10, CC^10, (i,j) -> i^2 + j^3*ii)
-eigenvalues m
-m = map(CC^10, CC^10, (i,j) -> (i+1)^(j+1))
-eigenvalues m
-m = map(RR^10, RR^10, (i,j) -> (i+1)^(j+1))
-eigenvalues m
-
-
 --
      R=ZZ/101[a..d]
      C=resolution cokernel vars R

M2/Macaulay2/tests/normal/eigenvectors.m2

@@ -0,0 +1,51 @@
+--
+M = matrix{{1.0,1.0},{0.0,1.0}}
+eigenvalues M
+eigenvectors M
+
+M0 = matrix{{1_RR,-1},{1,1}};
+M = M0++M0;
+(D, P) = eigenvectors M
+assert( 1e-10 > norm(M*P - P * diagonalMatrix(D)))
+
+M = matrix{{1.0, 2.0}, {2.0, 1.0}}
+eigenvectors(M, Hermitian=>true)
+
+M = matrix{{1.0, 2.0}, {5.0, 7.0}}
+(eigvals, eigvecs) = eigenvectors M
+-- here we use "norm" on vectors!
+assert( 1e-10 > norm ( M * eigvecs_0 - eigvals_0 * eigvecs_0 ) )
+assert( 1e-10 > norm ( M * eigvecs_1 - eigvals_1 * eigvecs_1 ) )
+
+printingPrecision = 2
+
+m = map(CC^10, CC^10, (i,j) -> i^2 + j^3*ii)
+(eigvals, eigvecs) = eigenvectors m
+max (abs \ eigvals) / min (abs \ eigvals)
+scan(#eigvals, i -> assert( 1e-10 > norm ( m * eigvecs_i - eigvals_i * eigvecs_i )))
+
+-- some ill-conditioned matrices
+
+m = map(CC^10, CC^10, (i,j) -> (i+1)^(j+1))
+(eigvals, eigvecs) = eigenvectors m
+max (abs \ eigvals) / min (abs \ eigvals)
+apply(#eigvals, i -> norm ( m * eigvecs_i - eigvals_i * eigvecs_i ))
+scan(#eigvals, i -> assert( 1e-4 > norm ( m * eigvecs_i - eigvals_i * eigvecs_i )))
+
+m = map(RR^10, RR^10, (i,j) -> (i+1)^(j+1))
+(eigvals, eigvecs) = eigenvectors m
+max (abs \ eigvals) / min (abs \ eigvals)
+apply(#eigvals, i -> norm ( m * eigvecs_i - eigvals_i * eigvecs_i ))
+scan(#eigvals, i -> assert( 1e-4 > norm ( m * eigvecs_i - eigvals_i * eigvecs_i )))
+
+
+
+--
+m = map(CC^10, CC^10, (i,j) -> i^2 + j^3*ii)
+eigenvalues m
+m = map(CC^10, CC^10, (i,j) -> (i+1)^(j+1))
+eigenvalues m
+m = map(RR^10, RR^10, (i,j) -> (i+1)^(j+1))
+eigenvalues m
+
+