Multiple Integral and Optimization in 3D

In summary, the conversation involved using a change of variables to solve an integral with parallel lines indicating this possibility. The person then asked for confirmation on their work for the second problem, which involved finding the minimum and maximum of a given function and using Lagrange multipliers to check the boundary. The final absolute extrema values were found to be 0 and 6+4sqrt(2), respectively.
  • #1
Vanrichten
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View attachment 1691

In this problem I have drawn out the region specified and noticed two sets of parallel lines indicating to me that a change of variable(u and v) are able to be used to solve this integral.

I decided that u=y-x and v = -2x-y then solving for x and y I obtain x= (u-v)/3 and y = (4u-v)/3

From here I can compute the jacobian but I want to be sure the work above is correct before I move on. Can anyone please confirm?

For this second problem can anyone verify that it has been completed correctly

View attachment 1692

View attachment 1693

I think I have the right absolute minimum and maximum. Can someone also verify ?
 

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  • #2
I would work the second problem as follows:

We are given the function:

\(\displaystyle f(x,y)=(x-1)^2+(y+1)^2\)

Computing the first partials, and equating them to zero, we find:

\(\displaystyle f_x(x,y)=2(x-1)=0\)

\(\displaystyle f_y(x,y)=2(y+1)=0\)

This yields the critical point $(x,y)=(1,-1)$, which is within $R$. Using the second partials test for relative extrema, we find:

\(\displaystyle f_{xx}(x,y)=2\)

\(\displaystyle f_{yy}(x,y)=2\)

\(\displaystyle f_{xy}(x,y)=0\)

Hence:

\(\displaystyle D(x,y)=4\)

Since \(\displaystyle f_{xx}(1,-1)=2>0\) and \(\displaystyle D(1,-1)=4>0\) we conclude this critical value is a relative minimum.

\(\displaystyle f(1,-1)=(1-1)^2+(-1+1)^2=0\)

To check the boundary, we may use Lagrange multipliers.

The objective function is:

\(\displaystyle f(x,y)=(x-1)^2+(y+1)^2\)

subject to the constraint:

\(\displaystyle g(x,y)=x^2+y^2-4=0\)

and this yields the system:

\(\displaystyle 2(x-1)=\lambda(2x)\)

\(\displaystyle 2(y+1)=\lambda(2y)\)

which implies:

\(\displaystyle \lambda=\frac{x-1}{x}=\frac{y+1}{y}\implies y=-x\)

Substituting for $y$ into the constraint, there results:

\(\displaystyle x^2+(-x)^2=4\)

\(\displaystyle x=\pm\sqrt{2},y=\mp\sqrt{2}\)

Thus, the extrema values of the function on the boundary are:

\(\displaystyle f(\sqrt{2},-\sqrt{2})=(\sqrt{2}-1)^2+(-\sqrt{2}+1)^2=6-4\sqrt{2}\)

\(\displaystyle f(-\sqrt{2},\sqrt{2})=(-\sqrt{2}-1)^2+(\sqrt{2}+1)^2=6+4\sqrt{2}\)

Thus, the absolute extrema of $f$ over $R$ are:

\(\displaystyle f_{\min}=f(1,-1)=0\)

\(\displaystyle f_{\max}=f(-\sqrt{2},\sqrt{2})=6+4\sqrt{2}\)
 

FAQ: Multiple Integral and Optimization in 3D

What is a multiple integral?

A multiple integral is a type of mathematical operation that involves calculating the area or volume under a function in multiple dimensions. It is an extension of the single integral, which is used to find the area under a curve in one dimension.

What is the purpose of using multiple integrals?

The main purpose of using multiple integrals is to solve optimization problems in three-dimensional space. This allows scientists to find the maximum or minimum values of a function in a given region, which is useful in many fields such as physics, engineering, and economics.

How is a multiple integral calculated?

A multiple integral is calculated by dividing the region of interest into small, infinitesimal pieces and summing up the contributions of these pieces. This can be done using different techniques such as double or triple integration, depending on the dimensionality of the problem.

What is the relationship between multiple integrals and optimization?

Multiple integrals are closely related to optimization because they allow us to find the maximum or minimum values of a function in a given region. This is done by setting up an optimization problem and using multiple integrals to solve it, which gives us the optimal solution.

What are some real-life applications of multiple integrals and optimization in 3D?

Multiple integrals and optimization in 3D have many real-life applications, such as determining the optimal shape of an airplane wing for maximum lift, finding the most efficient way to pack goods in a shipping container, and optimizing the design of a rollercoaster for the smoothest ride. They are also used in fields like finance and biology for problems involving three-dimensional data analysis and optimization.

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