The user has provided an optimization problem, asking whether the solution x=(7/3,0,1/3)T is optimal. The objective is to maximize 9x1+3x2+7x3, with specific constraints. No numerical algorithms can be used, so the proof of optimality likely requires linear programming theory, such as duality or complementary slackness.
Outlining the dual approach
To prove the optimality of the given solution without numerical algorithms, I'll use duality. First, I’ll express the primal problem in standard form: maximize cTx with constraints Ax≤b, x≥0. By formulating the dual, I’ll ensure the primal and dual objectives match and satisfy complementary slackness.
Exploring dual constraints
Now, I have the dual constraints: for each decision variable in the primal, I’ll write out corresponding inequalities for the dual variables. Then, I'll try to find values for the dual variables y1,y2,y3 that meet both the objective and constraints. I’ll check complementary slackness next by calculating slack values for each constraint.
Examining slack and dual variables
For the primal constraints, I computed the slack values and found that the first constraint isn’t tight, so y1 must be zero. The second constraint is tight, meaning y2 can be positive. The third constraint is also tight, so y3 might be positive as well. I've calculated y2=1.8.
По теореме сильной двойственности линейного программирования это возможно только в случае оптимальности обоих решений. Следовательно,
x∗=(37,0,31)⊤
является оптимальным, а максимальное значение целевой функции равно
max=370≈23,33.
Ни одного численного алгоритма (симплекс, внутренние точки и т. д.) не применялось — использованы лишь свойства двойственности и условия комплементарной нежёсткости.