Inner product space question

In summary: I am so sorry I am having a hard time understanding this. Please let me know if you don't understand what I am sayingIn summary, the problem states that in a complex inner product space V, if v is a vector and s is in a subset S of V, and <s,v> + <v,s> is less than or equal to <s,s>, then v is in the orthogonal set S\bot. This can be proven using the given inner product relations and the fact that if v is in the orthogonal complement of S, then <v,s> = <s,v> = 0. This means that <s,s> = <v,s> + <s,v> = 0, and
  • #1
rhobymic
5
0

Homework Statement



Let V be a complex inner product space and let S be a subset of V. Suppose that v in V is a vector for which
<s,v > + <v,s> [itex]\leq[/itex] <s,s>
Prove that v is in the orthogonal set S[itex]\bot[/itex]

Homework Equations



We have the three inner product relations:
1) conjugate symmetry
<x,y> = [itex]\overline{<y,x>}[/itex]

2) linearity
<x+y,z> = <x,z>+<y,z>

3) Def of a norm
||x|| = √<x,x>

There may be more that apply such as triangle inequality or the Cauchy–Schwarz inequality
but I am not sure

The Attempt at a Solution



I know that if v is in the set S[itex]\bot[/itex]
then s is orthogonal to v so <s,v> = <v,s> = 0
Therefor I am guessing through all these equations and the given inequality it can be shown that <s,v>+<v,s> needs to be both ≥ 0 and ≤ 0 therefor it will be zero

Am I thinking of the way forward correctly?
Any help towards a solution would be great!

Thanks
 
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  • #2
rhobymic said:

Homework Statement



Let V be a complex inner product space and let S be a subset of V. Suppose that v in V is a vector for which
<s,v > + <v,s> [itex]\leq[/itex] <s,s>
For all s in S? In that case, your conclusion is not true.
If v is orthogonal to S, this is saying that <s, s>= 0 for all s in S.

Prove that v is in the orthogonal set S[itex]\bot[/itex]

Homework Equations



We have the three inner product relations:
1) conjugate symmetry
<x,y> = [itex]\overline{<y,x>}[/itex]

2) linearity
<x+y,z> = <x,z>+<y,z>

3) Def of a norm
||x|| = √<x,x>

There may be more that apply such as triangle inequality or the Cauchy–Schwarz inequality
but I am not sure

The Attempt at a Solution



I know that if v is in the set S[itex]\bot[/itex]
then s is orthogonal to v so <s,v> = <v,s> = 0
Therefor I am guessing through all these equations and the given inequality it can be shown that <s,v>+<v,s> needs to be both ≥ 0 and ≤ 0 therefor it will be zero

Am I thinking of the way forward correctly?
Any help towards a solution would be great!

Thanks
 
  • #3
HallsofIvy said:
For all s in S? In that case, your conclusion is not true.
If v is orthogonal to S, this is saying that <s, s>= 0 for all s in S.

I did for get to state that this was for all s in S.

Could you explain a little more why <s,s>=0

would you be referring to the fact that we may be able to pick a case where v=s and therefor
2<s,s> ≤ <s,s>
and this would only happen when <s,s> = 0 because <x,x> ≥ 0?
 
  • #4
If v is in the orthogonal complement to S, then <v, s>= <s, v>= 0 so <s, s>= <v, s>+ <s, v>= 0+ 0= 0.

(No, I was not reffering to the case where v= s. If s is in S and v is in the orthogonal complement to S, then v= s only if v= s= 0.)
 
  • #5
HallsofIvy said:
If v is in the orthogonal complement to S, then <v, s>= <s, v>= 0 so <s, s>= <v, s>+ <s, v>= 0+ 0= 0.

(No, I was not reffering to the case where v= s. If s is in S and v is in the orthogonal complement to S, then v= s only if v= s= 0.)

We are given that <s, s>≥ <v, s>+ <s, v> not that they are equal so really saying that
<v, s>+ <s, v> =0 ( something we know will be true seeing as we are proving v is in the orthogonal complement of S) only shows that <s,s> can be 0 or anything greater than zero, not defiantly 0
 

1. What is an inner product space?

An inner product space is a mathematical concept that consists of a vector space equipped with an inner product function. The inner product function takes in two vectors and outputs a scalar value, representing the "angle" between the two vectors. This allows for the measurement of length, angles, and orthogonality within the vector space.

2. How is an inner product space different from a vector space?

An inner product space is a specific type of vector space. The main difference is that an inner product space has an additional structure in the form of the inner product function, which is not present in a general vector space. This allows for the concept of orthogonality and angle measurement, which are not defined in a vector space.

3. What are some examples of inner product spaces?

Some common examples of inner product spaces include Euclidean spaces, which are familiar 3-dimensional spaces with the standard dot product as the inner product function. Other examples include function spaces, such as the space of continuous functions, with the inner product being defined as the integral of the product of two functions.

4. What are the properties of an inner product space?

An inner product space has several key properties, including linearity in the first argument, symmetry, and positive definiteness. Linearity in the first argument means that the inner product function is linear with respect to the first vector in the input. Symmetry means that switching the order of the vectors does not change the output. Positive definiteness means that the inner product of a vector with itself is always positive, except for the zero vector where it is equal to zero.

5. How are inner product spaces used in practical applications?

Inner product spaces have many practical applications, particularly in the fields of physics, engineering, and computer science. They are used to model and analyze physical systems, such as quantum mechanics and signal processing. They also play a crucial role in optimization and machine learning algorithms, as well as in the analysis of data and images.

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