[pre-commit.ci] auto fixes from pre-commit.com hooks

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Author: pre-commit-ci[bot]

linear_programming/interior_point_method.py

@@ -11,15 +11,15 @@
 
 class InteriorPointMethod:
     """
-    Operate on linear programming problems using the Primal-Dual Interior-Point Method.
-
-    Attributes:
-        objective_coefficients (np.ndarray): Coefficient matrix for the objective
-function.
-        constraint_matrix (np.ndarray): Constraint matrix.
-        constraint_bounds (np.ndarray): Constraint bounds.
-        tol (float): Tolerance for stopping criterion.
-        max_iter (int): Maximum number of iterations.
+        Operate on linear programming problems using the Primal-Dual Interior-Point Method.
+
+        Attributes:
+            objective_coefficients (np.ndarray): Coefficient matrix for the objective
+    function.
+            constraint_matrix (np.ndarray): Constraint matrix.
+            constraint_bounds (np.ndarray): Constraint bounds.
+            tol (float): Tolerance for stopping criterion.
+            max_iter (int): Maximum number of iterations.
     """
 
     def __init__(
@@ -48,7 +48,7 @@ def _is_valid_input(self) -> bool:
 
     def _convert_to_standard_form(self) -> tuple[np.ndarray, np.ndarray]:
         """Convert constraints to standard form by adding slack and surplus
-variables."""
+        variables."""
         (m, n) = self.constraint_matrix.shape
         slack_surplus = np.eye(m)
         a_standard = np.hstack([self.constraint_matrix, slack_surplus])
@@ -57,11 +57,11 @@ def _convert_to_standard_form(self) -> tuple[np.ndarray, np.ndarray]:
 
     def solve(self) -> tuple[np.ndarray, float]:
         """
-        Solve the linear programming problem using the Primal-Dual Interior-Point
-Method.
+                Solve the linear programming problem using the Primal-Dual Interior-Point
+        Method.
 
-        Returns:
-            tuple: A tuple containing the optimal solution and the optimal value.
+                Returns:
+                    tuple: A tuple containing the optimal solution and the optimal value.
         """
         a, c = self._convert_to_standard_form()
         m, n = a.shape