Reconsider the linearly constrained convex programming model given in Prob. 13.9-10.

(a) If SUMT were to be applied to this problem, what would be the unconstrained function P(x; r) to be maximized at each iteration?

(b) Setting r 1 and using (

1 4

, 1 4

) as the initial trial solution, manually apply one iteration of the gradient search procedure (except stop before solving for t*) to begin maximizing the function P(x; r) you obtained in part (a).

D,C

(c) Beginning with the same initial trial solution as in part (b), use the automatic routine in your OR Courseware to apply SUMT to this problem with r 1, 102

, 104

.

(d) Compare the final solution obtained in part

(c) to the true optimal solution for Prob. 13.9-10 given in the back of the book.

What is the percentage error in x1, in x2, and in f(x)?