Use two-phase simplex to handle the constraint of the opposite inequality direction. Introduce nonnegative slack variables , a surplus variable , and an artificial variable . The equality constraints are
For Phase I maximize , initially with basis . Its initial simplex dictionary is
Choose to enter. The simplex ratio test compares , and , so leaves. The new dictionary is
Since , choose to enter. Its limiting ratios are from and from , so leaves. This gives
Now , so let enter. Its limiting ratios are from and from ; the artificial variable leaves. The simplex dictionary becomes
Setting all nonbasic variables to zero gives , a feasible original basis. Since , Phase I is optimal with value zero; delete .
For Phase II the original objective in this basis is
All nonbasic reduced costs are strictly negative, so no further improving simplex pivot exists. As the nonbasic variables are nonnegative, the dictionary also directly certifies , with equality only when . Therefore
The second original constraint has slack five and the other two are tight. As an independent linear programming optimality certificate, multiply the first constraint by and the third, after reversing it, by . This gives , and subtracting proves the same upper bound for the objective.

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