ENH avoid np.square(X) in enet_coordinate_descent to save memory (scikit-learn#31665)

Author: lorentzenchr

doc/whats_new/upcoming_changes/sklearn.linear_model/31665.enhancement.rst

@@ -0,0 +1,3 @@
+- class:`linear_model:ElasticNet` and class:`linear_model:Lasso` with
+  `precompute=False` uses less memory for dense `X` and is a bit faster.
+  By :user:`Christian Lorentzen <lorentzenchr>`

sklearn/linear_model/_cd_fast.pyx

@@ -139,7 +139,10 @@ def enet_coordinate_descent(
     cdef unsigned int n_features = X.shape[1]
 
     # compute norms of the columns of X
-    cdef floating[::1] norm_cols_X = np.square(X).sum(axis=0)
+    # same as norm_cols_X = np.square(X).sum(axis=0)
+    cdef floating[::1] norm_cols_X = np.einsum(
+        "ij,ij->j", X, X, dtype=dtype, order="C"
+    )
 
     # initial value of the residuals
     cdef floating[::1] R = np.empty(n_samples, dtype=dtype)