diff --git a/pysindy/optimizers/base.py b/pysindy/optimizers/base.py
index a45b9a488..c775837a5 100644
--- a/pysindy/optimizers/base.py
+++ b/pysindy/optimizers/base.py
@@ -49,6 +49,18 @@ class BaseOptimizer(LinearRegression, ComplexityMixin):
 
     copy_X : boolean, optional (default True)
         If True, X will be copied; else, it may be overwritten.
+
+    Attributes
+    ----------
+    coef_ : array, shape (n_features,) or (n_targets, n_features)
+        Weight vector(s).
+
+    ind_ : array, shape (n_features,) or (n_targets, n_features)
+        Array of 0s and 1s indicating which coefficients of the
+        weight vector have not been masked out.
+
+    history_ : list
+        History of ``coef_`` over iterations of the optimization algorithm.
     """
 
     def __init__(self, max_iter=20, normalize=False, fit_intercept=False, copy_X=True):
diff --git a/pysindy/optimizers/sr3.py b/pysindy/optimizers/sr3.py
index 53dc895bf..1a0acfe24 100644
--- a/pysindy/optimizers/sr3.py
+++ b/pysindy/optimizers/sr3.py
@@ -74,6 +74,11 @@ class SR3(BaseOptimizer):
         Weight vector(s) that are not subjected to the regularization.
         This is the w in the objective function.
 
+    history_ : list
+        History of sparse coefficients. ``history_[k]`` contains the
+        sparse coefficients (v in the optimization objective function)
+        at iteration k.
+
     Examples
     --------
     >>> import numpy as np
diff --git a/pysindy/optimizers/stlsq.py b/pysindy/optimizers/stlsq.py
index eab70efad..04cad3d95 100644
--- a/pysindy/optimizers/stlsq.py
+++ b/pysindy/optimizers/stlsq.py
@@ -47,12 +47,16 @@ class STLSQ(BaseOptimizer):
     Attributes
     ----------
     coef_ : array, shape (n_features,) or (n_targets, n_features)
-        Weight vector(s)
+        Weight vector(s).
 
     ind_ : array, shape (n_features,) or (n_targets, n_features)
         Array of 0s and 1s indicating which coefficients of the
         weight vector have not been masked out.
 
+    history_ : list
+        History of ``coef_``. ``history_[k]`` contains the values of
+        ``coef_`` at iteration k of sequentially thresholded least-squares.
+
     Examples
     --------
     >>> import numpy as np