Choose a set of column indices for which the square matrix is invertible. The associated basic solution sets for and solves . It is a basic feasible solution when all its coordinates are nonnegative.
Let be a basic feasible solution with basis . If for , then and nonnegativity force for every . Since and is invertible, . Thus is an extreme point.
Conversely, let be extreme and let . If , the corresponding columns are linearly dependent, so there is a nonzero vector supported on with . For sufficiently small , both and remain nonnegative and feasible, contradicting extremality. Hence . Enlarge to a set of indices. By the hypotheses is invertible, and is the resulting basic feasible solution. Therefore
Introduce slack variables after rewriting the third inequality as . The initial dictionary is
In the simplex method, enters first because it has the largest positive objective coefficient. The ratio test makes leave, and substitution gives
Next enters and leaves. Solving for produces the final objective row
All reduced costs are now nonpositive, so the dictionary is optimal at the nonbasic values . Therefore
The first constraint has slack , while the second and third constraints are active.

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