Find the critical points of the function f(x, y) = 8y2x − 8yx2 + 9xy. Determine whether they are local minima, local maxima, or saddle points. (Order your answers from smallest to largest x, then from smallest to largest y.)

Respuesta :

Answer:

Saddle point: [tex](0,0)[/tex]

Local minimum: [tex](\frac{3}{8}, -\frac{3}{8})[/tex]

Local maxima: [tex](0,-\frac{9}{8})[/tex], [tex](\frac{9}{8},0)[/tex]

Step-by-step explanation:

The function is:

[tex]f(x,y) = 8\cdot y^{2}\cdot x -8\cdot y\cdot x^{2} + 9\cdot x \cdot y[/tex]

The partial derivatives of the function are included below:

[tex]\frac{\partial f}{\partial x} = 8\cdot y^{2}-16\cdot y\cdot x+9\cdot y[/tex]

[tex]\frac{\partial f}{\partial x} = y \cdot (8\cdot y -16\cdot x + 9)[/tex]

[tex]\frac{\partial f}{\partial y} = 16\cdot y \cdot x - 8 \cdot x^{2} + 9\cdot x[/tex]

[tex]\frac{\partial f}{\partial y} = x \cdot (16\cdot y - 8\cdot x + 9)[/tex]

Local minima, local maxima and saddle points are determined by equalizing  both partial derivatives to zero.

[tex]y \cdot (8\cdot y -16\cdot x + 9) = 0[/tex]

[tex]x \cdot (16\cdot y - 8\cdot x + 9) = 0[/tex]

It is quite evident that one point is (0,0). Another point is found by solving the following system of linear equations:

[tex]\left \{ {{-16\cdot x + 8\cdot y=-9} \atop {-8\cdot x + 16\cdot y=-9}} \right.[/tex]

The solution of the system is (3/8, -3/8).

Let assume that y = 0, the nonlinear system is reduced to a sole expression:

[tex]x\cdot (-8\cdot x + 9) = 0[/tex]

Another solution is (9/8,0).

Now, let consider that x = 0, the nonlinear system is now reduced to this:

[tex]y\cdot (8\cdot y+9) = 0[/tex]

Another solution is (0, -9/8).

The next step is to determine whether point is a local maximum, a local minimum or a saddle point. The second derivative test:

[tex]H = \frac{\partial^{2} f}{\partial x^{2}} \cdot \frac{\partial^{2} f}{\partial y^{2}} - \frac{\partial^{2} f}{\partial x \partial y}[/tex]

The second derivatives of the function are:

[tex]\frac{\partial^{2} f}{\partial x^{2}} = 0[/tex]

[tex]\frac{\partial^{2} f}{\partial y^{2}} = 0[/tex]

[tex]\frac{\partial^{2} f}{\partial x \partial y} = 16\cdot y -16\cdot x + 9[/tex]

Then, the expression is simplified to this and each point is tested:

[tex]H = -16\cdot y +16\cdot x -9[/tex]

S1: (0,0)

[tex]H = -9[/tex] (Saddle Point)

S2: (3/8,-3/8)

[tex]H = 3[/tex] (Local maximum or minimum)

S3: (9/8, 0)

[tex]H = 9[/tex] (Local maximum or minimum)

S4: (0, - 9/8)

[tex]H = 9[/tex] (Local maximum or minimum)

Unfortunately, the second derivative test associated with the function does offer an effective method to distinguish between local maximum and local minimums. A more direct approach is used to make a fair classification:

S2: (3/8,-3/8)

[tex]f(\frac{3}{8} ,-\frac{3}{8} ) = - \frac{27}{64}[/tex] (Local minimum)

S3: (9/8, 0)

[tex]f(\frac{9}{8},0) = 0[/tex] (Local maximum)

S4: (0, - 9/8)

[tex]f(0,-\frac{9}{8} ) = 0[/tex] (Local maximum)

Saddle point: [tex](0,0)[/tex]

Local minimum: [tex](\frac{3}{8}, -\frac{3}{8})[/tex]

Local maxima: [tex](0,-\frac{9}{8})[/tex], [tex](\frac{9}{8},0)[/tex]