Trying to understand(conceptually) orthogonality

In summary, the problem is asking whether or not y^Tx = 0 or y^Ty = 0, given that Ax = b has a solution and A^Ty = 0. The fact that A and A^T are invertible may be helpful in determining the answer.
  • #1
Arnoldjavs3
191
3

Homework Statement


if Ax = b has a solution and A^Ty = 0 , is y^T(x) = 0 or y^T(y) =0

Homework Equations

The Attempt at a Solution


I simply do not think i understand the properties to answer this question.

From my understandinging, the transpose of A times y is = 0. This means that A transpose and y(all the members of these two subspaces) are perpendicular. Does this indicate that y is essentially the nullspace of A transpose? A transpose is the rowspace?

Where do I progress on frmo this?

I'd like to add... why is the transpose of these matrices even relevant? I don't understand. Are there properties of the transpose that I'm missing? What are these variables y and x representing? There's just so much here that's confusing me(I'm guessing I don't understand fundamentally)

I know that the row space is orthogonal to the nullspace(and colspace with the left nullspace) how do i use this knowledge to solve this problem?
 
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  • #2
Do you mean Ax=b has a unique solution?
If it has a unique solution then A is invertible, and hence so is ##A^T##, which allows us to choose between the two alternative answers.

But if we only know that Ax=b has at least one solution, then A may be singular, in which case the question is unanswerable.
 
  • #3
Yes it has a unique solution.

What good does it knowing that A^T and ax=b is invertible?
 
  • #4
You are given the equation ##A^Ty=0##. Using the fact that ##A## is invertible, and hence so is ##A^T##, rearrange that equation so that y is alone on one side (ie make y the subject of the equation). Then simplify as much as possible.
 
  • #5
Well if I were to isolate y from that equation you've stated, wouldn't it just be y = 0? the fact that A is invertible(meaning so is its transpose) confuses me. Why does that help me?
 
  • #6
They've asked you if ##y^Tx = 0## or ##y^Ty =0##. The answer is yes if either or both is true.
If you can show that ##y=0##, can you then prove that at least one of those two is true?

If so, the answer to the set question is Yes.
 
  • #7
Arnoldjavs3 said:

Homework Statement


if Ax = b has a solution and A^Ty = 0 , is y^T(x) = 0 or y^T(y) =0
What do y^T(x) and y^T(y) mean?
It would be helpful to state that x and y are vectors in some vector space (such as ##\mathbb{R}^n##?).

Since your question is about orthoganality, there's an inner product lurking about somewhere, so presumably y^T(x) could just as well be written as ##\vec{y} \cdot \vec{x}## and y^T(y) could be written as ##\vec{y} \cdot \vec{y}##
Arnoldjavs3 said:

Homework Equations

The Attempt at a Solution


I simply do not think i understand the properties to answer this question.

From my understandinging, the transpose of A times y is = 0. This means that A transpose and y(all the members of these two subspaces) are perpendicular.
That doesn't make any sense. Two vectors can be perpendicular (or othogonal), but a matrix and a vector aren't things that can be perpendicular to each other.
Arnoldjavs3 said:
Does this indicate that y is essentially the nullspace of A transpose? A transpose is the rowspace?
If ATy = 0, then, yes, y is in the nullspace of AT.
Arnoldjavs3 said:
Where do I progress on frmo this?

I'd like to add... why is the transpose of these matrices even relevant? I don't understand. Are there properties of the transpose that I'm missing? What are these variables y and x representing? There's just so much here that's confusing me(I'm guessing I don't understand fundamentally)

I know that the row space is orthogonal to the nullspace(and colspace with the left nullspace) how do i use this knowledge to solve this problem?
 
  • #8
Arnoldjavs3 said:

Homework Statement


if Ax = b has a solution and A^Ty = 0 , is y^T(x) = 0 or y^T(y) =0

Homework Equations

The Attempt at a Solution


I simply do not think i understand the properties to answer this question.

From my understandinging, the transpose of A times y is = 0. This means that A transpose and y(all the members of these two subspaces) are perpendicular. Does this indicate that y is essentially the nullspace of A transpose? A transpose is the rowspace?

Where do I progress on frmo this?

I'd like to add... why is the transpose of these matrices even relevant? I don't understand. Are there properties of the transpose that I'm missing? What are these variables y and x representing? There's just so much here that's confusing me(I'm guessing I don't understand fundamentally)

I know that the row space is orthogonal to the nullspace(and colspace with the left nullspace) how do i use this knowledge to solve this problem?
Could you post the problem exactly as given to you? It seems like you're leaving a lot of relevant details out.
 

What is orthogonality?

Orthogonality refers to the concept of two things being completely independent or unrelated. In mathematics, it is often used to describe two vectors or geometric lines that are perpendicular to each other.

Why is orthogonality important?

Orthogonality is important in many disciplines, including mathematics, physics, and engineering. It allows for simplification of complex problems and helps to identify relationships between different variables. In computer science, orthogonality is a key principle in designing efficient and modular systems.

What are some practical examples of orthogonality?

Some practical examples of orthogonality include a door hinge, which allows the door to rotate while remaining perpendicular to the frame, and a coordinate system, where the x and y axes are orthogonal to each other. In linear algebra, orthogonal matrices are used to simplify calculations and solve systems of equations.

How does orthogonality relate to machine learning?

In machine learning, orthogonality is used to describe the independence of features or variables in a dataset. This allows for more accurate predictions and reduces the risk of overfitting. Orthogonal transformation is also used in dimensionality reduction techniques, such as principal component analysis.

How can one achieve orthogonality in their work?

To achieve orthogonality in a project or problem, one must carefully consider the relationships between different variables and strive for independence. This may involve breaking down a complex problem into smaller, orthogonal parts or using techniques such as Gram-Schmidt orthogonalization to transform a set of vectors into an orthogonal basis.

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