normalize eigenvector for numerical running purposes

2 files changed, 5 insertions, 1 deletions

diff --git a/src/eigen.c b/src/eigen.c
index 83b1127..36ccc92 100644
--- a/src/eigen.c
+++ b/src/eigen.c
@@ -31,6 +31,10 @@ double dominant_eigenvalue(Matrix_double *m, Array_double *v, double tolerance,
 
   while (error >= tolerance && (--iter) > 0) {
     Array_double *eigenvector_2 = m_dot_v(m, eigenvector_1);
+    Array_double *normalized_eigenvector_2 =
+        scale_v(eigenvector_2, 1.0 / linf_norm(eigenvector_2));
+    free_vector(eigenvector_2);
+    eigenvector_2 = normalized_eigenvector_2;
 
     Array_double *mx = m_dot_v(m, eigenvector_2);
     double new_lambda =
diff --git a/test/eigen.t.c b/test/eigen.t.c
index 31c3500..f271bf2 100644
--- a/test/eigen.t.c
+++ b/test/eigen.t.c
@@ -25,7 +25,7 @@ UTEST(eigen, leslie_matrix_dominant_eigenvalue) {
   Array_double *felicity = InitArray(double, {0.0, 1.5, 0.8});
   Array_double *survivor_ratios = InitArray(double, {0.8, 0.55});
   Matrix_double *leslie = leslie_matrix(survivor_ratios, felicity);
-  Array_double *v_guess = InitArrayWithSize(double, 3, 1.0);
+  Array_double *v_guess = InitArrayWithSize(double, 3, 2.0);
   double tolerance = 0.0001;
   uint64_t max_iterations = 64;