What Directions at Point (2, 0) Make the Rate of Change -1 for f(x, y) = xy?

In summary, to determine the directions at the point (2,0) in which the function f(x,y) = xy has a rate of change of -1, we first find the gradient of the function at that point, which is (0,2). Then, using the equation D_u(f)(a,b) = ∇f(a,b) · (u1,u2), we can determine that u2 = -0.5. However, for u1, there are two possible values depending on whether u is a unit vector or not. If it is a unit vector, then u1 can only be ±√3/2. Otherwise, there will be infinitely many possible values for u1.
  • #1
ilyas.h
60
0

Homework Statement

In what directions at the point (2, 0) does the function f(x, y) = xy have rate of change -1?[itex]D_{u}(f)(a,b) = \bigtriangledown f(a,b)\cdot (u_{1}, u_{2}) [/itex]

[itex]f(x,y) = xy[/itex]

[itex](a,b) = (2,0).[/itex]

The Attempt at a Solution


[itex]\frac{\partial f}{\partial x} = y[/itex]

[itex]\frac{\partial f}{\partial y} = x[/itex]

[itex]\bigtriangledown f(2,0) = (\frac{\partial f}{\partial x}, \frac{\partial f}{\partial y})= (y, x) = (0, 2)[/itex]

plugging in:

[itex]D_{u}(xy)(2,0) = \bigtriangledown f(0,2)\cdot (u_{1},u_{2}) = -1[/itex]

[itex](0,2)\cdot (u_{1},u_{2}) = -1[/itex]

[itex]u_{2} = -0.5[/itex]

[itex]u_{1}[/itex] has infinitely many values.

the last line above is the part I am confused about. Are there infinitely many values for u1? thanks.
 
Last edited:
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  • #2
ilyas.h said:

Homework Statement

In what directions at the point (2, 0) does the function f(x, y) = xy have rate of change -1?[itex]D_{u}(f)(a,b) = \bigtriangledown f(a,b)\cdot (u_{1}, u_{2}) [/itex]

[itex]f(x,y) = xy[/itex]

[itex](a,b) = (2,0).[/itex]

The Attempt at a Solution


[itex]\frac{\partial f}{\partial x} = y[/itex]

[itex]\frac{\partial f}{\partial y} = x[/itex]

[itex]\bigtriangledown f(2,0) = (\frac{\partial f}{\partial x}, \frac{\partial f}{\partial y})= (y, x) = (0, 2)[/itex]

plugging in:

[itex]D_{u}(xy)(2,0) = \bigtriangledown f(0,2)\cdot (u_{1},u_{2}) = -1[/itex]

[itex](0,2)\cdot (u_{1},u_{2}) = -1[/itex]

[itex]u_{2} = -0.5[/itex]

[itex]u_{1}[/itex] has infinitely many values.

the last line above is the part I am confused about. Are there infinitely many values for u1? thanks.

Do you want/need ##\vec{u} = (u_1,u_2)## to be a unit vector? If so, there are only two possible values of ##u_1##; if not, there will be infinitely many values of ##u_1##. As before, you need to use additional information if you have it.
 
  • #3
Ray Vickson said:
Do you want/need ##\vec{u} = (u_1,u_2)## to be a unit vector? If so, there are only two possible values of ##u_1##; if not, there will be infinitely many values of ##u_1##. As before, you need to use additional information if you have it.

in the equation:

[itex]D_{u}(f)(a,b) = \bigtriangledown f(a,b)\cdot (u_{1}, u_{2}) [/itex]

the (u1, u2) is a unit vector (according to my lecture notes). So you suggest that u1 has two possible values. How so? and I posted all the information there is in the Q in the thread, so no missing links. I am quite confused,

edit: I think I know now, from definition of a unit vector: (u1)^2 + (u2)^2 = 1^2

plug in and you'll get u1 = +/- (root3)/2
 
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Related to What Directions at Point (2, 0) Make the Rate of Change -1 for f(x, y) = xy?

1. What is a directional derivative?

A directional derivative is a mathematical concept used to calculate the rate of change of a function in a specific direction. It allows us to find the slope of a function in a certain direction at a given point.

2. How is a directional derivative calculated?

The directional derivative is calculated using the gradient of the function and the unit vector in the direction of interest. It can be expressed as the dot product of the gradient vector and the unit vector in the desired direction.

3. What is the significance of directional derivatives in real life?

Directional derivatives are used in various fields such as physics, engineering, and economics to analyze the rate of change of a function in a particular direction. They are particularly useful in optimization problems, where the goal is to find the maximum or minimum value of a function.

4. Can a directional derivative be negative?

Yes, a directional derivative can be negative. It depends on the direction of interest and the slope of the function in that direction. A negative directional derivative indicates that the function is decreasing in the given direction.

5. How is a directional derivative different from a partial derivative?

A directional derivative is a generalization of a partial derivative. While a partial derivative measures the rate of change of a function in a specific direction (i.e. along one of the coordinate axes), a directional derivative can be calculated in any direction. In other words, a partial derivative is a special case of a directional derivative.

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