Consider the problem minimise f(x1, x2, x3) = x² + x²x² + 2x1x2 + x² +8x2 subject to 2x1 + 5x₂ + x3 = 3. (a) Use the reduced gradient to determine which of the following are stationary points: (i) (0, 0, 2); (ii) (0,0,3); (iii) (1,0,1). (b) For each staionary point you found in part (a), use the reduce Hessian to deter- mine whether they are a local minima, a local maxima or a local saddle.
Consider the problem minimise f(x1, x2, x3) = x² + x²x² + 2x1x2 + x² +8x2 subject to 2x1 + 5x₂ + x3 = 3. (a) Use the reduced gradient to determine which of the following are stationary points: (i) (0, 0, 2); (ii) (0,0,3); (iii) (1,0,1). (b) For each staionary point you found in part (a), use the reduce Hessian to deter- mine whether they are a local minima, a local maxima or a local saddle.
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
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Transcribed Image Text:Consider the problem
minimise f(x1,x2, x3) = x² + x1x² + 2x1x₂ + x² +8x₂
subject to 2x1 + 5x2 + x3 = 3.
(a) Use the reduced gradient to determine which of the following are stationary
points: (i) (0, 0, 2); (ii) (0,0,3); (iii) (1,0,1).
(b) For each staionary point you found in part (a), use the reduce Hessian to deter-
mine whether they are a local minima, a local maxima or a local saddle.
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