Inner Product Space and Orthogonality proof question (is this the correct way?)

In summary, if w is orthogonal to each of the vectors u1, u2,...,ur, then it is orthogonal to every vector in the span{u1,u2,...,ur}.
  • #1
ryan8642
24
0

Homework Statement


Let V be the inner product space. Show that if w is orthogonal to each of the vectors u1, u2,...,ur, then it is orthogonal to every vector in the span{u1,u2,...,ur}.


Homework Equations



u.v=0 to be orthogonal
If u and v are vectors in an inner product space, then ||u+v||^2 = ||u||^2 + ||v||^2

The Attempt at a Solution


So we know ||u+v||^2 = ||u||^2 + ||v||^2 for ortho vectors

for u1: <u1,w>=0
||u1+w||^2 = <u1+w, u1+w>
= <u1,u1> + 2<u1,w> + <w,w>
= ||u1||^2 + ||w||^2

then i do the same for u2 and ur.

Is this the correct way to go about this?

Thanks
 
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  • #2
ryan8642 said:

Homework Statement


Let V be the inner product space. Show that if w is orthogonal to each of the vectors u1, u2,...,ur, then it is orthogonal to every vector in the span{u1,u2,...,ur}.


Homework Equations



u.v=0 to be orthogonal
If u and v are vectors in an inner product space, then ||u+v||^2 = ||u||^2 + ||v||^2

The Attempt at a Solution


So we know ||u+v||^2 = ||u||^2 + ||v||^2 for ortho vectors

for u1: <u1,w>=0
||u1+w||^2 = <u1+w, u1+w>
= <u1,u1> + 2<u1,w> + <w,w>
= ||u1||^2 + ||w||^2

then i do the same for u2 and ur.

Is this the correct way to go about this?

Thanks

No, it isn't. What's the definition of span?
 
  • #3
So w has to be orthogonal to all of the vectors in the span{u1,u2,...,ur}

k1u1+k2u2+...+krur=b to test for spanning where b is an arbitrary vector.
would 'b' be w in this case?
 
  • #4
ryan8642 said:
So w has to be orthogonal to all of the vectors in the span{u1,u2,...,ur}

k1u1+k2u2+...+krur=b to test for spanning where b is an arbitrary vector.
would 'b' be w in this case?

No, b would be an arbitrary vector in span{u1,u2,...,ur}. So you want to show b.w=0. What is (k1u1+k2u2+...+krur).w?
 
  • #5
i am sooo confused.

b and w are both vectors.
so b=(b1,b2,...br)
w=(w1,w2,...wr) w vector is perpendicular to b vector (b lies in the span of u1,u2,...ur)

so b1w1+b2w2+...+brwr=0

*i noticed u edited what u asked me.

What is (k1u1+k2u2+...+krur).w?

k1u1w1+k2u2w2+...+krurwr = 0 (im assuming it has to equal zero to be orthogonal)
 
  • #6
ryan8642 said:
i am sooo confused.

b and w are both vectors.
so b=(b1,b2,...br)
w=(w1,w2,...wr) w vector is perpendicular to b vector (b lies in the span of u1,u2,...ur)

so b1w1+b2w2+...+brwr=0

Look b=(k1u1+k2u2+...+krur). That's an arbitrary vector in the span. The k's are numbers and the u's are vectors. w is just w. Compute (k1u1+k2u2+...+krur).w by using the distributive property. You know (a+b).c=a.c+b.c where a,b,c are vectors, yes? You also know (xa).c=x(a.c) where a and c are vectors and x is a number, yes? Use those properties.
 
  • #7
Compute (k1u1+k2u2+...+krur).w
=w.k1u1 + w.k2u2 + ... + w.krur
=k1(w.u1) + k2(w.u2) + ... + kr(w.ur)

sorry about bein really dumb with this, I am horrible with proofs. but good with numbers...
 
  • #8
ryan8642 said:
Compute (k1u1+k2u2+...+krur).w
=w.k1u1 + w.k2u2 + ... + w.krur
=k1(w.u1) + k2(w.u2) + ... + kr(w.ur)

sorry about bein really dumb with this, I am horrible with proofs. but good with numbers...

I'm sure you are good with numbers. 'Abstract' throws some people. Just pretend they are numbers. Now what are w.u1, w.u2, etc?
 
  • #9
w.u1=0
w.u2=0
..
w.ur=0

they are all orthogonal to each other.

=k1(w.u1) + k2(w.u2) + ... + kr(w.ur)
=k1(0) +k2(0) +...+ kr(0)
=0

and btw man i really appreciate u walkin me through this proof. This helps me sooo much for this proof and just solving proofs in general.
 
  • #10
btw, u must know proofs well and understand them well.
Do you have any tips for studying them/remembering proofs for tests/exams?
 
  • #11
ryan8642 said:
btw, u must know proofs well and understand them well.
Do you have any tips for studying them/remembering proofs for tests/exams?

I'll give you one that's pretty important. Look up the definition of any terms you are a little vague on. That's why my first hint was "What's the definition of span?". Your first attempt didn't even use that. That's a pretty good hint you are missing something. Other than that, practice makes perfect.
 
  • #12
ok, i will for sure do that while I am studying.
Thanks for all the help.
 

FAQ: Inner Product Space and Orthogonality proof question (is this the correct way?)

What is an inner product space?

An inner product space is a vector space equipped with an inner product, which is a mathematical operation that takes two vectors as inputs and produces a scalar as output. It has properties such as linearity, symmetry, and positive definiteness.

What is orthogonality in an inner product space?

In an inner product space, two vectors are considered orthogonal if their inner product is equal to zero. Geometrically, this means the vectors are perpendicular to each other. Orthogonality is an important concept in many areas of mathematics, including linear algebra and functional analysis.

How do you prove orthogonality in an inner product space?

To prove orthogonality in an inner product space, you need to show that the inner product of the two vectors is equal to zero. This can be done by using the properties of the inner product, such as linearity and symmetry, and manipulating the equations until you arrive at the desired result.

What is the difference between an inner product space and a normed vector space?

An inner product space has an inner product operation defined on it, while a normed vector space has a norm operation defined on it. The norm operation measures the length or magnitude of a vector, while the inner product measures the angle between two vectors. Not all normed vector spaces are inner product spaces, but all inner product spaces are normed vector spaces.

Why is orthogonality important in inner product spaces?

Orthogonality is important in inner product spaces because it allows us to define concepts such as perpendicularity, projection, and distance. It also has many practical applications, such as in signal processing, image compression, and solving systems of linear equations. Additionally, orthogonality has connections to other important mathematical concepts, such as eigenvalues and eigenvectors.

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