Project - constrained optimization 2.0 1.5 1.0 0.5 y 0.0 -0.5 -1.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 Figure 1.14: Contours of f and a plot of g = 0. Given a function f(x, y) = 1+x? + y? – 4 x y and a constraint g(x, y) = x² + y³ – 2 = 0, %3D find: 1. all local minima of f, 2. all local minima of f under the constraint g(x, y) = 0 3. all local maxima of f under the constraint g(x, y) = 0 4. one global maxima of f under the constraint g(x, y) = 0

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Chapter6: Linear Systems
Section6.8: Linear Programming
Problem 39E
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Project - constrained optimization
2.0
1.5
1.0
0.5
y
0.0
-0.5
-1.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
Figure 1.14: Contours of f and a plot of g = 0.
Given a function
f(x, y) = 1+ x² + y? – 4 x y
and a constraint
g(x, y) = x² + y³ – 2 = 0,
find:
1. all local minima of f,
2. all local minima of f under the constraint g(x, y)
= 0
3. all local maxima of f under the constraint g(x, y) = 0
4. one global maxima of f under the constraint g(x, y) = 0
Hint: For local constrained minimization or maximization use the method of Lagrange multipliers.
Alternatively, you can also express y = y(x) from g(x, y) = 0 and replace f(x, y) by a functions of
one variable f(x, y(x)).
Transcribed Image Text:Project - constrained optimization 2.0 1.5 1.0 0.5 y 0.0 -0.5 -1.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 Figure 1.14: Contours of f and a plot of g = 0. Given a function f(x, y) = 1+ x² + y? – 4 x y and a constraint g(x, y) = x² + y³ – 2 = 0, find: 1. all local minima of f, 2. all local minima of f under the constraint g(x, y) = 0 3. all local maxima of f under the constraint g(x, y) = 0 4. one global maxima of f under the constraint g(x, y) = 0 Hint: For local constrained minimization or maximization use the method of Lagrange multipliers. Alternatively, you can also express y = y(x) from g(x, y) = 0 and replace f(x, y) by a functions of one variable f(x, y(x)).
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