is optimal for: minimize subject to , , . If is an optimal dual solution, then
Tap an option to check your answer.
is optimal for: minimize subject to , , . If is an optimal dual solution, then
Tap an option to check your answer.
Answer: (B)
Both constraints are binding at . Since , the dual constraint for holds with equality, giving ; since , the dual constraint for gives , so .
Thus and , which satisfies , option (B).
The two shadow prices are not equal; complementary slackness pins them at 1 and 2, so the correct relation is .
(x₁* = 1, x₂* = 0, x₃* = 2) is optimal for: minimize x₁ + 5x₂ + 2x₃ subject to x₁ - x₂ ≤ 1, x₁ + x₂ + x₃ ≥ 3, x₁, x₂, x₃ ≥ 0. If (λ₁*, λ₂*) is an optimal dual solution, then (A) 2λ₁* = 3λ₂* (B) 2λ₁* = λ₂* (C) λ₁* = 2λ₂* (D) λ₁* = λ₂*
Also written as: (x_1* = 1, x_2* = 0, x_3* = 2) is optimal for: minimize x_1 + 5x_2 + 2x_3 subject to x_1 - x_2 <= 1, x_1 + x_2 + x_3 >= 3, x_1, x_2, x_3 >= 0. If (lambda_1*, lambda_2*) is an optimal dual solution, then
Constrained optimisation and linear programming has come up 44 times in 7 exams since 2007.
This question is on Linear programming duality, part of Constrained optimisation and linear programming in Mathematics.
Pairing a linear program with its dual and what their solutions say about each other.
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Question from the IIT JAM 2021 paper. Solutions are TestStop's own. Not affiliated with or endorsed by any examination authority.