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Python-Ensemble-Toolbox · Jul 2, 2024 · 36f3485 · 36f3485
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diff --git a/pipt/update_schemes/es.py b/pipt/update_schemes/es.py
@@ -91,9 +91,9 @@ class es_approx(esMixIn, enkf_approx):
     pass
 
 
-class es_full(esMixIn, enkf_approx):
+class es_full(esMixIn, enkf_full):
     """
-    mixin of ES class and approx update.
+    mixin of ES class and full update.
     Note that since we do not iterate there is no difference between is full and approx.
     """
     pass

diff --git a/popt/README.md b/popt/README.md
@@ -6,6 +6,7 @@ Currently, the following methods are implemented:
 - EnOpt: The standard ensemble optimization method
 - GenOpt: Generalized ensemble optimization (using non-Gaussian distributions)
 - SmcOpt: Gradient-free optimization based on sequential Monte Carlo
+- LineSearch: Gradient based method satisfying the strong Wolfie conditions
 
 The gradient and Hessian methods are compatible with SciPy, and can be used as input to scipy.optimize.minimize. 
 A POPT tutorial is found [here](https://github.com/Python-Ensemble-Toolbox/PET/blob/main/docs/tutorials/popt/tutorial_popt.ipynb).