8 orthogonal projection innequality

I'm not sure what you're studying, but you should be able to solve this problem too.In summary, we are given a subspace U in R4 and a vector v outside of U. We are asked to find the closest vector u0 in U to v. By visualizing U as a plane and v as a vector perpendicular to the plane, we can use the property of perpendicular vectors to find the projection of v onto U, which will give us the closest vector u0 in U to v. This can be done by finding the orthogonal projection of v onto U, which can be calculated using the basis vectors of U.
  • #1
nhrock3
415
0
8)
[itex]U=\{x=(x_{1},x_{2},x_{3},x_{4})\in R^{4}|x_{1}+x_{2}+x_{4}=0\}[/itex]
is a subspace of [itex]R^{4}[/itex]
[itex]v=(2,0,0,1)\in R^{4}[/itex]
find [itex]u_{0}\in U[/itex] so [itex]||u_{0}-v||<||u-v||[/itex]
how i tried:
[itex]U=sp\{(-1,1,0,0),(-1,0,0,1),(0,0,1,0)\}[/itex]
i know that the only [itex]u_{0}[/itex] for which this innequality will work
is if it will be the orthogonal projection on U parallel to v
i am not sure about the theory of finding it
what to do next?
 
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  • #2
nhrock3 said:
8)
[tex]U=\{x=(x_{1},x_{2},x_{3},x_{4})\in R^{4}|x_{1}+x_{2}+x_{4}=0\}[/tex]
is a subspace of [itex]R^{4}[/itex]
[itex]v=(2,0,0,1)\in R^{4}[/itex]
find [itex]u_{0}\in U[/itex] so [itex]||u_{0}-v||<||u-v||[/itex]
how i tried:
[itex]U=sp\{(-1,1,0,0),(-1,0,0,1),(0,0,1,0)\}[/itex]
OK, this is a basis for U.
nhrock3 said:
i know that the only [itex]u_{0}[/itex] for which this innequality will work
is if it will be the orthogonal projection on U parallel to v
i am not sure about the theory of finding it
what to do next?
You can think of U as being a plane through the origin in R4. It doesn't hurt to visualize this as a plane in three dimensions. U is actually a hyperplane, being a space of one dimension less than the space it's in, but it's more convenient to think of this problem as a plane in R3.

Vector v is not in the "plane" (U), so the vector u0 that is closest to v will be the vector that is directly under v, and lying in U. In other words, u0 is the projection of v onto the "plane" U. Surely in what you're studying there are some examples of how to find the projection of one vector onto another vector or a vector onto a plane.
 
  • #3
actually there no examples
:)
 
  • #4
Here's a drawing to help you out.
plane.PNG


The drawing shows the subspace U as a plane. Vector v sticks up out of the plane, starting from the origin in R4. Vector u0 lies in the plane, and its tail is also at the origin.

Vector w is perpendicular to the plane.

The three vectors shown form a triangle, so B]u[/B]0 + w = v.
The triangle is a right triangle, so u0 [itex]\cdot[/itex] w = 0.
Any vector in the plane U can be written as a linear combination of the vectors in the basis you found. What must be true of every vector in the plane in relation to a vector that is perpendicular to the plane?

I was able to solve this problem after I drew the picture shown here.
 

Related to 8 orthogonal projection innequality

1. What is the concept of 8 orthogonal projection inequality?

The 8 orthogonal projection inequality is a mathematical concept that involves the use of linear algebra to determine the minimum distance between two subspaces. It is often used in optimization problems and has applications in fields such as computer science, engineering, and economics.

2. How is the 8 orthogonal projection inequality calculated?

The 8 orthogonal projection inequality can be calculated using the Gram-Schmidt process, which involves finding a set of orthonormal vectors that span the two subspaces. The minimum distance between the subspaces is then determined by finding the length of the projection of one subspace onto the other.

3. What is the significance of the 8 orthogonal projection inequality?

The 8 orthogonal projection inequality is significant because it allows for the determination of the closest possible approximation between two subspaces. This has practical applications in fields such as image and signal processing, where it is used to improve the accuracy of data analysis and interpretation.

4. Can the 8 orthogonal projection inequality be applied to real-world problems?

Yes, the 8 orthogonal projection inequality has numerous real-world applications. For example, it can be used in data compression to reduce the amount of data needed to store or transmit information without significant loss of accuracy. It is also used in machine learning algorithms to improve the efficiency and accuracy of data classification.

5. Are there any limitations to the 8 orthogonal projection inequality?

While the 8 orthogonal projection inequality is a powerful tool, it does have limitations. It assumes that the subspaces are finite-dimensional and does not account for non-linear relationships between variables. Additionally, it may not be applicable in cases where the subspaces are not well-defined or have a high degree of overlap.

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