use scipy's eigenvalue solver to get smallest eigenvalue

HERA-Team · Feb 14, 2025 · c4b784c · c4b784c
1 parent e1c15e4
commit c4b784c
Showing 1 changed file with 4 additions and 8 deletions.
diff --git a/hera_filters/dspec.py b/hera_filters/dspec.py
@@ -2987,20 +2987,16 @@ def sparse_linear_fit_2D(
 
         # Compute the preconditioner for the first axis
         XTX_axis_1 = np.dot(axis_1_basis.T.conj() * axis_1_wgts, axis_1_basis)
-        eigenval, _ = np.linalg.eig(XTX_axis_1)
-        axis_1_lambda = np.min(
-            eigenval[eigenval.real > np.finfo(eigenval.dtype).eps].real * eig_scaling_factor
-        )
+        eigenval = sparse.linalg.eigs(XTX_axis_1, k=1, which='SR', return_eigenvectors=False) 
+        axis_1_lambda = eigenval.real * eig_scaling_factor
         axis_1_pcond = np.linalg.pinv(
             XTX_axis_1 + np.eye(XTX_axis_1.shape[0]) * axis_1_lambda
         )
 
         # Compute the preconditioner for the second axis
         XTX_axis_2 = np.dot(axis_2_basis.T.conj() * axis_2_wgts, axis_2_basis)
-        eigenval, _ = np.linalg.eig(XTX_axis_2)
-        axis_2_lambda = np.min(
-            eigenval[eigenval.real > np.finfo(eigenval.dtype).eps].real * eig_scaling_factor
-        )
+        eigenval = sparse.linalg.eigs(XTX_axis_2, k=1, which='SR', return_eigenvectors=False) 
+        axis_2_lambda = eigenval.real * eig_scaling_factor
         axis_2_pcond = np.linalg.pinv(
             XTX_axis_2 + np.eye(XTX_axis_2.shape[0]) * axis_2_lambda
         )