Explaning Orthogonality: Vectors, Subspaces, and Curiosity

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In summary: It makes more sense now.In summary, the equation U\oplus U^{\bot}=V says that every vector in V but not in U is a unique sum of a vector in U and a vector in U^{\perp}.
  • #1
talolard
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Homework Statement


Hello,
Please pardon me if my terms are off, I'm studying in hebrew and might lacka few of the english words.
Anyway this isn't homewokr but just curiosity.
We learned in class that if V is a vector space and U is a subspace of V then [tex] U\oplus U^{\bot}=V [/tex]
But then it seems to me that this implies that every vector that is in V but not in U is orthogonal to every vector in U. i.e.

This just strikes me as odd and counterintuitive. Is it correct or am I issing something.
Thanks
Tal

The Attempt at a Solution

 
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  • #2
I don't find it counterintuitive.. for instance, let the subspace U be some plane in a 3d-V. Then all you need to get V is U and its normal.
And it doesn't say that every vector in V but not in U is orthogonal to U, but rather that with U and the vectors orthogonal to U spans V.
I think the U= some plane in 3d-V-space is the best and most intuitive example i can think of.
 
  • #3
talolard said:

Homework Statement


Hello,
Please pardon me if my terms are off, I'm studying in hebrew and might lacka few of the english words.
Anyway this isn't homewokr but just curiosity.
We learned in class that if V is a vector space and U is a subspace of V then [tex] U\oplus U^{\bot}=V [/tex]
But then it seems to me that this implies that every vector that is in V but not in U is orthogonal to every vector in U. i.e.
No, that's not what it is saying. [itex]U\oplus U^{\bot}[/itex] is NOT a union of sets. In particular, it does NOT mean that every vector in V is in one or the other of those. It means, rather, that every vector in V is a unique sum of a vector in U and a vector in [itex]U^{\perp}[/itex].


Suppose V is the xy-plane and U is the x-axis. Then [itex]U^{\perp}[/itex] is the y- axis. Every vector in V, [itex]a\vec{i}+ b\vec{j}[/itex] is a unique sum of a vector in U and a vector in V- here, just [itex]a\vec{i}[/itex] and [itex]b\vec{j}[/itex], respectively.

A slightly harder example: With V as before, let U= {(x,y)|y= x}, the line y= x. Then [itex]U^{\perp}[/itex] is {(x,y)| y= -x}. Given a vector [itex]p\vec{i}+ q\vec{j}[/itex] how would we write it as a sum of vectors in U and [itex]U^{\perp}[/itex]? Well, any vector in U is of the form [itex]a\vec{i}+ a\vec{j}[/itex] for some number a and every vector in [itex]U^{\perp}[/itex] is of the form [itex]b\vec{i}- b\vec{j}[/itex] for some number b so we would need to find a and b such that [itex](a\vec{i}+ a\vec{j})+ (b\vec{i}- b\vec{j}= p\vec{i}+ q\vec{j}[/itex].

That is, [itex](a+ b)\vec{i}+ (a- b)\vec{j}= p\vec{i}+ q\vec{j}[/itex] so we must have a+ b= p and a- b= q. Adding the two equations, 2a= p+ q so a= (p+ q)/2. Subtracting the two equations, 2b= p- q so b= (p- q)/2.

That is, any vector in R2 can be written, in a unique way, as the sum of a vector in U and a vector in [itex]U^{\perp}[/itex].

This just strikes me as odd and counterintuitive. Is it correct or am I missing something.
Thanks
Tal

The Attempt at a Solution

 
  • #4
Thanks for clearing that up.
 

FAQ: Explaning Orthogonality: Vectors, Subspaces, and Curiosity

What is orthogonality in mathematics?

Orthogonality in mathematics refers to the concept of two objects being perpendicular to each other. In the context of vectors, it means that two vectors are at a 90 degree angle to each other. This can also apply to other mathematical objects such as subspaces, where it means that the subspaces do not share any common space.

How are vectors and orthogonality related?

Vectors are directly related to orthogonality because they can be used to represent and visualize the concept. In a two-dimensional space, two vectors are orthogonal if they are at a 90 degree angle to each other. In higher dimensions, vectors can still be orthogonal if their dot product is equal to zero.

What are some real-world applications of orthogonality?

Orthogonality has many practical applications in fields such as engineering, physics, and computer science. In engineering, it is used to determine the forces acting on structures. In physics, it is used to calculate the components of forces and motion. In computer science, it is used in algorithms and data structures.

How is orthogonality related to subspaces?

In linear algebra, subspaces are defined as vector spaces that are contained within a larger vector space. Orthogonality is related to subspaces because subspaces can be orthogonal to each other. This means that the subspaces do not share any common space, and their dimensions are independent of each other.

Why is orthogonality important in mathematics?

Orthogonality is a fundamental concept in mathematics that has many important applications. It is used to solve systems of equations, find the components of vectors, and perform transformations in geometric spaces. It also has practical applications in various fields, making it a crucial concept to understand in mathematics.

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