Multivariable calculus question

In summary: Can you make it be -5?Yeah, pretty much.The dot product of two vectors ##\vec{A}## and ##\vec{B}## is given by ##\vec{A}\cdot\vec{B} = \|\vec{A}\| \|\vec{B}\| \cos \theta##, where ##\theta## is the angle between them. Clearly, the product must be between ##-\|\vec{A}\| \|\vec{B}\|## and ##\|\vec{A}\| \|\vec{B}\|##. In this problem, this means that the directional derivative must be between -4 and +4. Can you make it be -5?No
  • #1
kwal0203
69
0

Homework Statement



A function [itex]f(x,y)=\mathbb{R}^{2}\rightarrow \mathbb{R}[/itex] is defined by:

[itex]f(x,y)=5y^{2}-x^{2}[/itex]

(i) Find its gradient vector and evaluate it at the point [itex](x,y)^{T}=(1,1)^{T}[/itex]. Find the rate of change of the function in the direction [itex](2,1)^{T}[/itex] at the point [itex](1,1)^{T}[/itex]

(ii) In what direction is the rate of change of the function at the point [itex](x,y)^{T}=(2,0)^{T}[/itex] equal to [itex]-2[/itex]?

Is there a direction for which the rate of change at the point [itex](x,y)^{T}=(2,0)^{T}[/itex] is equal to [itex]-5[/itex]? Find the greatest rate of decrease of the function at this point.

The Attempt at a Solution



(i)

[itex]f(x,y)=5y^{2}-x^{2}[/itex]

[itex]f_{x}(x,y)=-2x=-2[/itex] at (1,1)

[itex]f_{y}(x,y)=10y=10[/itex] at (1,1)

The unit vector in the direction of [itex](2,1)^{T}[/itex] is [itex](2,1)^{T}=\frac{2}{\sqrt{5}},\frac{1}{\sqrt{5}}[/itex]

To find the rate of change in the direction of the unit vector above I:

[itex]<\bigtriangledown f,u>=f_{x}u_{1}+f_{y}u_{2}=-2\cdot \frac{2}{\sqrt{5}}+10\cdot \frac{1}{\sqrt{5}}=\frac{6}{\sqrt{5}}[/itex]

(ii)

at [itex](2,0)^{T}[/itex] the gradient vector is:

[itex]\bigtriangledown f=(-4,0)[/itex]

so knowing [itex](\bigtriangledown f,u)=-2[/itex] gives

[itex]-2=-4\cdot u_{1}+0\cdot u_{2}[/itex]

[itex]-2=-4\cdot u_{1}[/itex]

[itex]u_{1}=\frac{1}{2}[/itex]

so the direction in which the rate of change of the function is [itex]u=(\frac{1}{2},0)[/itex]

(iia) for the part where it asks about the rate of change being [itex]-5[/itex] I used the same method and got [itex]u=(\frac{5}{4},0)[/itex].

Is this all correct?

but now I don't know how to 'Find the greatest rate of decrease of the function at this point.'

Any help appreciated thanks!
 
Last edited:
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  • #2
kwal0203 said:

Homework Statement



A function [itex]f(x,y)=\mathbb{R}^{2}\rightarrow \mathbb{R}[/itex] is defined by:

[itex]f(x,y)=5y^{2}-x^{2}[/itex]

(i) Find its gradient vector and evaluate it at the point [itex](x,y)^{T}=(1,1)^{T}[/itex]. Find the rate of change of the function in the direction [itex](2,1)^{T}[/itex] at the point [itex](1,1)^{T}[/itex]

(ii) In what direction is the rate of change of the function at the point [itex](x,y)^{T}=(2,0)^{T}[/itex] equal to [itex]-2[/itex]?

Is there a direction for which the rate of change at the point [itex](x,y)^{T}=(2,0)^{T}[/itex] is equal to [itex]-5[/itex]? Find the greatest rate of decrease of the function at this point.

The Attempt at a Solution



(i)

[itex]f(x,y)=5y^{2}-x^{2}[/itex]

[itex]f_{x}(x,y)=-2x=-2[/itex] at (1,1)

[itex]f_{y}(x,y)=10y=10[/itex] at (1,1)

The unit vector in the direction of [itex](2,1)^{T}[/itex] is [itex](2,1)^{T}=\frac{2}{\sqrt{5}},\frac{1}{\sqrt{5}}[/itex]

To find the rate of change in the direction of the unit vector above I:

[itex]<\bigtriangledown f,u>=f_{x}u_{1}+f_{y}u_{2}=-2\cdot \frac{2}{\sqrt{5}}+10\cdot \frac{1}{\sqrt{5}}=\frac{6}{\sqrt{5}}[/itex]

(ii)

at [itex](2,0)^{T}[/itex] the gradient vector is:

[itex]\bigtriangledown f=(-4,0)[/itex]

so knowing [itex](\bigtriangledown f,u)=-2[/itex] gives

[itex]-2=-4\cdot u_{1}+0\cdot u_{2}[/itex]

[itex]-2=-4\cdot u_{1}[/itex]

[itex]u_{1}=\frac{1}{2}[/itex]

so the direction in which the rate of change of the function is [itex]u=(\frac{1}{2},0)[/itex]
Actually, you don't know that ##u_2=0##. You want to use the fact that ##\vec{u}## is a unit vector to determine ##u_2##.

(iia) for the part where it asks about the rate of change being [itex]-5[/itex] I used the same method and got [itex]u=(\frac{5}{4},0)[/itex].

Is this all correct?

but now I don't know how to 'Find the greatest rate of decrease of the function at this point.'

Any help appreciated thanks!
 
  • #3
Oh I see, so:

[itex]\parallel u \parallel=1=\sqrt{\frac{1}{2}\cdot \frac{1}{2}+u_{2}^{2}}[/itex]

[itex]\frac{1}{4}+u_{2}^{2}=1^{2}[/itex]

[itex]u_{2}^{2}=1-\frac{1}{4}[/itex]

[itex]u_{2}=\sqrt{\frac{3}{4}}[/itex]

[itex]u_{2}=\pm \frac{1}{2}\sqrt{3}[/itex]

[itex]u=(\frac{1}{2},\pm \frac{1}{2}\sqrt{3})[/itex]
 
  • #4
Yup, so can you see now that the first answer to iia is wrong?
 
  • #5
vela said:
Yup, so can you see now that the first answer to iia is wrong?

I think so:

[itex]u=(\frac{1}{2},\pm \frac{1}{2}\sqrt{3})[/itex]

[itex]\bigtriangledown f=(-4,0)[/itex]

so,

[itex](\bigtriangledown f,u)=-4\cdot \frac{1}{2}+0[/itex]

[itex]u=(-2,0)[/itex]?
 
  • #6
For iia?

kwal0203 said:
(iia) for the part where it asks about the rate of change being [itex]-5[/itex] I used the same method and got [itex]u=(\frac{5}{4},0)[/itex].

Is this all correct?

but now I don't know how to 'Find the greatest rate of decrease of the function at this point.'

Any help appreciated thanks!
The gradient evaluated at the point is (-4,0), and now you're dotting a unit vector into it. When is the dot product the greatest? What is the biggest value ##(-4,0)\cdot\vec{u}## can take on when ##\vec{u}## is a unit vector?
 
  • #7
vela said:
For iia?The gradient evaluated at the point is (-4,0), and now you're dotting a unit vector into it. When is the dot product the greatest? What is the biggest value ##(-4,0)\cdot\vec{u}## can take on when ##\vec{u}## is a unit vector?

I'm not sure. When is the dot product the greatest?

You mean when [itex]\left \| \bigtriangledown f \right \|=\sqrt{-4^{2}+0}[/itex]

[itex]\left \| \bigtriangledown f \right \|=4[/itex]?

then,

[itex]-\left \| \bigtriangledown f \right \|=-4[/itex]?

so,

[itex]-4[/itex] is the minimum and there is no direction that can make the rate of change [itex]-5[/itex]
 
Last edited:
  • #8
Yeah, pretty much.

The dot product of two vectors ##\vec{A}## and ##\vec{B}## is given by ##\vec{A}\cdot\vec{B} = \|\vec{A}\| \|\vec{B}\| \cos \theta##, where ##\theta## is the angle between them. Clearly, the product must be between ##-\|\vec{A}\| \|\vec{B}\|## and ##\|\vec{A}\| \|\vec{B}\|##. In this problem, this means that the directional derivative must be between -4 and +4.
 

1. What is multivariable calculus?

Multivariable calculus is a branch of mathematics that deals with functions of more than one variable. It involves the study of rates of change, optimization, and integration in multiple dimensions.

2. What are the applications of multivariable calculus?

Multivariable calculus has various applications in fields such as physics, engineering, economics, and computer graphics. It is used to model and analyze complex systems with multiple variables, such as fluid flow, electric fields, and optimization problems.

3. What are the main concepts in multivariable calculus?

The main concepts in multivariable calculus include partial derivatives, multiple integrals, and theorems such as the gradient theorem, divergence theorem, and Stokes' theorem. These concepts are used to solve problems involving functions with multiple variables.

4. How is multivariable calculus different from single variable calculus?

In single variable calculus, the functions have only one independent variable, while in multivariable calculus, the functions have multiple independent variables. This means that the concepts and techniques used in multivariable calculus are more complex and require a deeper understanding of functions and their behavior in multiple dimensions.

5. What are some tips for understanding and solving multivariable calculus problems?

Some tips for understanding and solving multivariable calculus problems include visualizing the problem, breaking down the problem into smaller parts, and practicing with a variety of problems. It is also important to have a strong foundation in single variable calculus and to understand the underlying concepts and theorems in multivariable calculus.

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