Proof about inner product spaces

In summary, in a real inner-product space V, if (v1, ..., vm) is a linearly independent list of vectors, there exist exactly 2^m orthonormal lists (e1, ..., em) such that span(v1, ..., vj) = span(e1, ..., ej) for all j ∈ {1, ..., m}. This can be proven by considering orthogonal bases and choosing e_j to be either v_j or its additive opposite, resulting in a doubling of the number of orthonormal lists for each additional vector added to the basis.
  • #1
evilpostingmong
339
0

Homework Statement


Suppose V is a real inner-product space and (v1, . . . , vm) is a
linearly independent list of vectors in V. Prove that there exist
exactly 2^m orthonormal lists (e1, . . . , em) of vectors in V such
that
span(v1, . . . , vj) = span(e1, . . . , ej)
for all j ∈ {1, . . . , m}.

Homework Equations


The Attempt at a Solution



Alright, just to be sure I have the right idea (this is not the proof attempt)
I'll consider an orthogonal basis {u1} with j=1 so the non orthogonal basis for V
is {v1}.
I guess that if j=1 then let u1=v1 so e1=u1/llu1ll so two possible orthonormal lists are {e1} and{-e1} then after adding an additional element to the basis {v1} (call it
v2) j becomes 2, the non-orthogonal becomes {v1,v2}, the orthogonal basis is {u1,u2}
and u2=v2-proj_u2(v2) and e2=u2/llu2ll (with the other possible orthonormal basis
being -e2) but since e1 and -e1 cannot share
the same list, they must be put in separate (but similar) lists which double
the amount of lists from when j was 1 so now we have {e1, e2} and {e1, -e2}
and {-e1, e2} finally {-e1, -e2} four possible orthonormal lists. If m=j, that's it,
but if not, eventually when it does, we will keep multiplying 2^j by 2 as each vj is added to the basis {v1..vj-1}.
This isn't the proof, just want to see if I'm seeing this right.
 
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  • #2


Are you sure you stated the problem right? Consider, for example, that any two-dimensional real inner-product space has infinitely many orthonormal bases.

(edit: aha, nevermind)
 
Last edited:
  • #3


Yeah, that's the basic idea. Given [itex]e_1,..., e_{j-1},[/itex] there are two choices for [itex]e_j[/itex] and they are additive opposites of each other
 

What is an inner product space?

An inner product space is a mathematical concept in linear algebra that refers to a vector space equipped with an inner product, which is a generalization of the dot product. This inner product allows for the definition of geometric concepts such as length, angle, and orthogonality in the vector space.

What is the importance of inner product spaces?

Inner product spaces are important because they provide a framework for studying geometric properties of vector spaces. They also have many applications in physics, engineering, and other areas of mathematics.

What are the axioms of an inner product space?

The axioms of an inner product space are linearity in the first argument, symmetry, and positive definiteness. Linearity means that the inner product is linear in the first argument and follows the properties of a linear transformation. Symmetry means that the inner product of two vectors is equal to the inner product of the same vectors in reverse order. Positive definiteness means that the inner product of a vector with itself is always a positive real number.

How is the inner product of two vectors calculated?

The inner product of two vectors is calculated by taking the sum of the products of their corresponding components. For example, in a two-dimensional vector space with vectors A=(a1,a2) and B=(b1,b2), the inner product would be a1b1+a2b2. In a higher-dimensional vector space, the inner product would be the sum of the products of all corresponding components.

What is the Cauchy-Schwarz inequality and how is it related to inner product spaces?

The Cauchy-Schwarz inequality states that the absolute value of the inner product of two vectors is always less than or equal to the product of their lengths. In other words, the length of the projection of one vector onto the other is always less than or equal to the length of the vector being projected. This inequality is a fundamental property of inner product spaces and has many important applications in mathematics and physics.

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