diff --git a/README.md b/README.md
index ab8ec9d..2b9dacc 100644
--- a/README.md
+++ b/README.md
@@ -26,18 +26,18 @@ For a lack of a better name, this repository calls these algorithms:
 The following describes how to use this repository in your own implementation.
 
 ## The set function
-To use this in your own code, your function to be maximized should be contained in an object of a class inheriting from `AbstractObjectiveFunction`. This class looks as follows:
+To use this in your own code, your function to be maximized should be contained in an object of a class inheriting from `AbstractSubmodularFunction`. This class looks as follows:
 ``` Python 
-class AbstractObjectiveFunction:
+class AbstractSubmodularFunction:
     def evaluate(self, input_set: Set[E]) -> float:
         raise NotImplementedError('Abstract Method')
 ```
-That is, `AbstractObjectiveFunction` requires its subclasses to implement an `evaluate()` method, taking as input a `Set[E]` and resulting in a `float`. This method should evaluate the set function on the given set, returning the value of the function. This class corresponds to the *'value oracle'*, which should be able to return the value of the function to be maximixed for every possible subset of the *ground set*.
+That is, `AbstractSubmodularFunction` requires its subclasses to implement an `evaluate()` method, taking as input a `Set[E]` and resulting in a `float`. This method should evaluate the set function on the given set, returning the value of the function. This class corresponds to the *'value oracle'*, which should be able to return the value of the function to be maximixed for every possible subset of the *ground set*.
 
-Typically, your own class inheriting `AbstractObjectiveFunction` can contain instance variables for parameters required by the objective function.
+Typically, your own class inheriting `AbstractSubmodularFunction` can contain instance variables for parameters required by the objective function.
 
 ## The Optimizers
-Every included optimizer inherits the class `AbstractOptimizer`. Each optimizer should be iniitialized with at least two arguments:
+Every included optimizer inherits the class `AbstractOptimizer`. Each optimizer should be initialized with at least two arguments:
 1. the objective function to be optimized
 2. the ground set of items. The optimizers will search over the power set of this ground set.
 
@@ -45,8 +45,8 @@ The following shows the definition of the `AbstractOptimizer` class:
 
 ``` Python
 class AbstractOptimizer:
-    def __init__(self, objective_function: AbstractObjectiveFunction, ground_set: Set[E], debug: bool = True):
-        self.objective_function: AbstractObjectiveFunction = objective_function
+    def __init__(self, objective_function: AbstractSubmodularFunction, ground_set: Set[E], debug: bool = True):
+        self.objective_function: AbstractSubmodularFunction = objective_function
         self.ground_set: Set[E] = ground_set
         self.debug: bool = debug
 
@@ -93,7 +93,7 @@ Some good references for submodular maximization
 > 
 > Buchbinder, N., & Feldman, M. (2019). Submodular Functions Maximization Problems. Handbook of Approximation Algorithms and Metaheuristics, Second Edition, 753–788. https://doi.org/10.1201/9781351236423-42
 
-Andreas Krause and Carlos Guestrin maintain a [great website about submodular optimization and the submodularity property](https://las.inf.ethz.ch/submodularity/)
+Andreas Krause and Carlos Guestrin maintain a [great website about submodular optimization and the submodularity property](https://las.inf.ethz.ch/submodularity/), linking to their [Matlab/Octave toolbox for Submodular Function Optimization](https://las.inf.ethz.ch/sfo/index.html).
 
 Jan Vondrak hosts the [slides for some great presentations he did about submodular functions on his website.](https://theory.stanford.edu/~jvondrak/presentations.html)
 
diff --git a/submodmax/abstract_optimizer.py b/submodmax/abstract_optimizer.py
index 2751e0b..09278f5 100644
--- a/submodmax/abstract_optimizer.py
+++ b/submodmax/abstract_optimizer.py
@@ -3,14 +3,14 @@
 E = TypeVar('E')
 
 
-class AbstractObjectiveFunction:
+class AbstractSubmodularFunction:
     def evaluate(self, input_set: Set[E]) -> float:
         raise NotImplementedError('Abstract Method')
 
 
 class AbstractOptimizer:
-    def __init__(self, objective_function: AbstractObjectiveFunction, ground_set: Set[E], debug: bool = True):
-        self.objective_function: AbstractObjectiveFunction = objective_function
+    def __init__(self, objective_function: AbstractSubmodularFunction, ground_set: Set[E], debug: bool = True):
+        self.objective_function: AbstractSubmodularFunction = objective_function
         self.ground_set: Set[E] = ground_set
         self.debug: bool = debug
 
diff --git a/submodmax/deterministic_double_greedy_search.py b/submodmax/deterministic_double_greedy_search.py
index 91e1ca0..7bf3036 100644
--- a/submodmax/deterministic_double_greedy_search.py
+++ b/submodmax/deterministic_double_greedy_search.py
@@ -1,6 +1,6 @@
 from typing import Set, TypeVar
 
-from .abstract_optimizer import AbstractOptimizer, AbstractObjectiveFunction
+from .abstract_optimizer import AbstractOptimizer, AbstractSubmodularFunction
 
 E = TypeVar('E')
 
@@ -23,7 +23,7 @@ class DeterministicDoubleGreedySearch(AbstractOptimizer):
 
     """
 
-    def __init__(self, objective_function: AbstractObjectiveFunction, ground_set: Set[E], debug: bool = True):
+    def __init__(self, objective_function: AbstractSubmodularFunction, ground_set: Set[E], debug: bool = True):
         super().__init__(objective_function, ground_set, debug)
 
     def optimize(self) -> Set[E]:
diff --git a/submodmax/deterministic_local_search.py b/submodmax/deterministic_local_search.py
index d3c2c78..7143201 100644
--- a/submodmax/deterministic_local_search.py
+++ b/submodmax/deterministic_local_search.py
@@ -1,6 +1,6 @@
 from typing import Set, Tuple, Optional, TypeVar
 
-from .abstract_optimizer import AbstractOptimizer, AbstractObjectiveFunction
+from .abstract_optimizer import AbstractOptimizer, AbstractSubmodularFunction
 
 E = TypeVar('E')
 
@@ -27,7 +27,7 @@ class DeterministicLocalSearch(AbstractOptimizer):
     FOCS paper: https://people.csail.mit.edu/mirrokni/focs07.pdf (page 4-5)
     """
 
-    def __init__(self, objective_function: AbstractObjectiveFunction, ground_set: Set[E], epsilon: float = 0.05,
+    def __init__(self, objective_function: AbstractSubmodularFunction, ground_set: Set[E], epsilon: float = 0.05,
                  debug: bool = True):
         super().__init__(objective_function, ground_set, debug)
         self.epsilon: float = epsilon
diff --git a/submodmax/deterministic_local_search_pyids.py b/submodmax/deterministic_local_search_pyids.py
index 2b1f687..d8a68e2 100644
--- a/submodmax/deterministic_local_search_pyids.py
+++ b/submodmax/deterministic_local_search_pyids.py
@@ -1,6 +1,6 @@
 from typing import Set, Tuple, Optional, TypeVar
 
-from .abstract_optimizer import AbstractOptimizer, AbstractObjectiveFunction
+from .abstract_optimizer import AbstractOptimizer, AbstractSubmodularFunction
 
 E = TypeVar('E')
 
@@ -28,7 +28,7 @@ class DeterministicLocalSearchPyIDS(AbstractOptimizer):
     This implementation is largely based on the one from Jiri Filip and Tomas Kliegr included in PyIDS.
     """
 
-    def __init__(self, objective_function: AbstractObjectiveFunction, ground_set: Set[E], epsilon=0.05,
+    def __init__(self, objective_function: AbstractSubmodularFunction, ground_set: Set[E], epsilon=0.05,
                  debug: bool = True):
         super().__init__(objective_function, ground_set, debug)
         self.epsilon: float = epsilon
diff --git a/submodmax/randomized_double_greedy_search.py b/submodmax/randomized_double_greedy_search.py
index 16a8bd4..2c622e5 100644
--- a/submodmax/randomized_double_greedy_search.py
+++ b/submodmax/randomized_double_greedy_search.py
@@ -3,7 +3,7 @@
 
 import numpy as np
 
-from .abstract_optimizer import AbstractOptimizer, AbstractObjectiveFunction
+from .abstract_optimizer import AbstractOptimizer, AbstractSubmodularFunction
 
 E = TypeVar('E')
 
@@ -30,7 +30,7 @@ class RandomizedDoubleGreedySearch(AbstractOptimizer):
 
     """
 
-    def __init__(self, objective_function: AbstractObjectiveFunction, ground_set: Set[E], debug: bool = True):
+    def __init__(self, objective_function: AbstractSubmodularFunction, ground_set: Set[E], debug: bool = True):
         super().__init__(objective_function, ground_set, debug)
 
     def optimize(self) -> Set[E]:
diff --git a/submodmax/smooth_local_search.py b/submodmax/smooth_local_search.py
index 3023e26..3f32a48 100644
--- a/submodmax/smooth_local_search.py
+++ b/submodmax/smooth_local_search.py
@@ -4,7 +4,7 @@
 
 import numpy as np
 
-from .abstract_optimizer import AbstractOptimizer, AbstractObjectiveFunction
+from .abstract_optimizer import AbstractOptimizer, AbstractSubmodularFunction
 from .random_set import sample_a_set_with_bias_delta_on_A
 
 E = TypeVar('E')
@@ -27,7 +27,7 @@ class SmoothLocalSearch(AbstractOptimizer):
     Note: the problem of maximizing a submodular function is NP-hard.
 
     """
-    def __init__(self, objective_function: AbstractObjectiveFunction, ground_set: Set[E], debug=True):
+    def __init__(self, objective_function: AbstractSubmodularFunction, ground_set: Set[E], debug=True):
         super().__init__(objective_function, ground_set, debug)
 
         self.empty_set: Set[E] = set()
diff --git a/submodmax/smooth_local_search_pyids.py b/submodmax/smooth_local_search_pyids.py
index 89d89b7..73f2b2c 100644
--- a/submodmax/smooth_local_search_pyids.py
+++ b/submodmax/smooth_local_search_pyids.py
@@ -4,7 +4,7 @@
 
 import numpy as np
 
-from .abstract_optimizer import AbstractOptimizer, AbstractObjectiveFunction
+from .abstract_optimizer import AbstractOptimizer, AbstractSubmodularFunction
 from .random_set import sample_a_set_with_bias_delta_on_A, RandomSetOptimizer
 
 E = TypeVar('E')
@@ -29,7 +29,7 @@ class SmoothLocalSearchPyIDS(AbstractOptimizer):
     This implementation is largely based on the one from Jiri Filip and Tomas Kliegr included in PyIDS.
     """
 
-    def __init__(self, objective_function: AbstractObjectiveFunction, ground_set: Set[E], debug: bool = True):
+    def __init__(self, objective_function: AbstractSubmodularFunction, ground_set: Set[E], debug: bool = True):
         super().__init__(objective_function, ground_set, debug)
         self.rs_optimizer = RandomSetOptimizer(ground_set)