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

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@@ -333,7 +333,9 @@ The **graphical method** for solving simple linear programming (LP) problems inv
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### [Key]() Concepts:
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**[Decision Variables]():** These are the variables that we want to determine the values of to optimize the ***[OBJECTIVE FUNCTION]()*** (e.g.):
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<br><br>
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- **[Decision Variables]():** These are the variables that we want to determine the values of to optimize the ***[OBJECTIVE FUNCTION]()*** (e.g.):
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<br>
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- **[Constraints]():** These are linear inequalities or equalities that restrict the values the decision variables can take.
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<br>
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- [Equality constraint]():
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<br><br>
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- **[Equality constraint]():**
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<br>
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$$a_{i1}x_1 + a_{i2}x_2$$ = $$b_i$$
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$a_{i1}x_1 + a_{i2}x_2$ = $b_i$
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<br>
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```latex
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a_{i1}x_1 + a_{i2}x_2 = b_i
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```
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<br><br>
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- [Less than or equal to constraint]():
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- **[Less than or equal to constraint]():**
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$$a_{i1}x_1$$ + $$a_{i2}x_2 \leq b_i$$
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$a_{i1}x_1$$ + $a_{i2}x_2 \leq b_i$$
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```latex
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a_{i1}x_1 + a_{i2}x_2 \leq b_i
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```
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- [Greater than or equal to constraint]():
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5. **[Determine the Optimal Solution]():**
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* For a **maximization** problem, the vertex that yields the **largest** value of the objective function is the optimal solution [1, 6].
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* For a **minimization** problem, the vertex that yields the **smallest** value of the objective function is the optimal solution [2, 7].
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* For a **minimization** problem, the vertex that yields the **smallest** value of the objective function is the optimal solution [2, 7].
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<br><br>

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