Update README.md

Signed-off-by: Fabiana  🧬 Campanari <113218619+FabianaCampanari@users.noreply.github.com>

Author: FabianaCampanari

README.md

@@ -439,24 +439,24 @@ $a_{i1}x_1$ + $a_{i2}x_2 \geq b_i$
 
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-2. **[Identify the Feasible Region]():** For each inequality constraint, determine which side of the line satisfies the inequality. This can be done by testing a point (e.g., the origin $\(0,0)\)$; if it's not on the line) in the inequality. The feasible region is the area where all the shaded regions of the inequalities overlap. If there are non-negativity constraints $\(x_1 \geq 0\)$ and $\(x_2 \geq 0\)$, the feasible region will be in the **[first quadrant]()** [3].
+2. **[Identify the Feasible Region]():** For each inequality constraint, determine which side of the line satisfies the inequality. This can be done by testing a point (e.g., the origin $\(0,0)\)$; if it's not on the line) in the inequality. The feasible region is the area where all the shaded regions of the inequalities overlap. If there are non-negativity constraints $\(x_1 \geq 0\)$ and $\(x_2 \geq 0\)$, the feasible region will be in the **[first quadrant]()**.
 
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-3.  **[Identify the Vertices]():** Determine the coordinates of all the vertices (corner points) of the feasible region [4, 6]. These are the points where the boundary lines intersect.
+3.  **[Identify the Vertices]():** Determine the coordinates of all the vertices (corner points) of the feasible region. These are the points where the boundary lines intersect.
 
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-4.  **[Evaluate the Objective Function at Each Vertex]():** Substitute the coordinates of each vertex into the objective function to find the value of the objective function at that point [6].
+4.  **[Evaluate the Objective Function at Each Vertex]():** Substitute the coordinates of each vertex into the objective function to find the value of the objective function at that point.
 
 
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 5.  **[Determine the Optimal Solution]():**
 
-    *   For a **maximization** problem, the vertex that yields the **largest** value of the objective function is the optimal solution [1, 6].
+    *   For a **maximization** problem, the vertex that yields the **largest** value of the objective function is the optimal solution.
 
-     *   For a **minimization** problem, the vertex that yields the **smallest** value of the objective function is the optimal solution [2, 7].
+    *   For a **minimization** problem, the vertex that yields the **smallest** value of the objective function is the optimal solution.
 
 
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