Make some linting

simplex.cpp

@@ -142,41 +142,3 @@ Result simplex(Vector &C, Matrix &A, Vector &b, double eps = 0.01, bool maximize
 	}
 	return result;
 }
-
-/*
-Function_name(C, A, b, eps = eps_default)
-
-Input:
-- C: A vector of coefficients of the objective function
-- A: A matrix of coefficients of the constraint functions
-- b: A vector of right-hand side values
-- eps: Approximation accuracy (optional, default = eps_default)
-
-Steps:
-1. Print the optimization problem:
-   - max (or min) z = C[0] * x1 + C[1] * x2 + ... + C[n] * xn
-   - subject to the constraints:
-	 - A[0] * x <= b[0]
-	 - A[1] * x <= b[1]
-	 - ...
-	 - A[m] * x <= b[m]
-
-2. Initialize:
-   - Form the initial tableau by introducing slack variables to convert inequalities into equalities.
-
-3. Iteratively apply the Simplex method:
-   - Step 1: Identify the entering variable (most negative coefficient in the objective row).
-   - Step 2: Identify the leaving variable (smallest positive ratio of RHS to pivot column).
-   - Step 3: Perform pivot operations to update the tableau.
-
-4. Check for optimality or unboundedness:
-   - If all coefficients in the objective function row are non-negative, the solution is optimal.
-   - If no leaving variable exists, the problem is unbounded.
-
-5. Return:
-   - solver_state: {solved, unbounded}
-   - x*: Optimal vector of decision variables (if solved)
-   - z: Maximum (or minimum) value of the objective function (if solved)
-
-End Function
-*/