Set , so and . Introduce nonnegative slack and surplus variables :The objective is . A feasible simplex basis is with nonbasic variables ; its basic values are . The initial simplex dictionary isIn the simplex algorithm, choose as the entering variable since it has positive objective coefficient. With the other nonbasic variables zero, only decreases; the simplex ratio test gives . Thus leaves. Pivoting its row givesAll nonbasic variables are nonnegative, and all objective coefficients in the final dictionary are nonpositive, proving optimality. Setting them to zero gives , , , henceThe surplus value is , so the lower constraint is indeed satisfied, not mistakenly treated as equality at the optimum. The strict negative objective coefficients also show uniqueness of the optimizer.
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