A Modified Basis in an Inner Product Space

In summary, an Inner Product Space is a vector space with a function that takes in two vectors and outputs a scalar. A Basis in an Inner Product Space is a set of vectors that span the space and are linearly independent. A Modified Basis is created by multiplying each basis vector by a non-zero scalar, and is used to simplify calculations in the Inner Product Space. The Gram-Schmidt Process is a method for finding a modified basis by performing orthogonalization and normalization steps on a set of linearly independent vectors.
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Given an orthonormal basis ##\{e_1,\ldots, e_n\}## in a complex inner product space ##V## of dimension ##n##, show that if ##v_1,\ldots, v_n\in V## such that ##\sum_{j = 1}^n \|v_j\|^2 < 1##, then ##\{v_1 + e_1,\ldots, v_n + e_n\}## is a basis for ##V##.
 
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Made a mistake
 
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We prove linear independence. First, we have ##\| v_i + e_i \| \geq \| e_i \| - \| v_i \| > 0##, so none of the ##\{ v_1 + e_1 , \dots , v_n + e_n \}## is the zero vector. We wish to prove that if

$$
\sum_{i=1}^n \alpha_i (v_i + e_i) = 0
$$

then ##\alpha_i = 0## for ##i = 1 , \dots , n##. We assume that all the ##\alpha##'s being zero is not the only solution and arrive at a contradiction. The above condition implies

$$
\| \sum_{i=1}^n \alpha_i e_i \|^2 = \| \sum_{i=1}^n \alpha_i v_i \|^2 .
$$

We have

\begin{align*}
\| \sum_{i=1}^n \alpha_i v_i \|^2 & \leq (\sum_{i=1}^n | \alpha_i | \cdot \| v_i \| )^2
\nonumber \\
& \leq ( \sum_{i=1}^n |\alpha_i|^2) ( \sum_{i=1}^n \| v_i \|^2 )
\nonumber \\
& < \sum_{i=1}^n | \alpha_i |^2 .
\end{align*}

Note strict inequality. However,

$$
\| \sum_{i=1}^n \alpha_i e_i \|^2 = \sum_{i=1}^n | \alpha_i |^2
$$

and so we have a contradiction. The only way out of the contradiction is for all the ##\alpha##'s to be zero.


A standard result is that any set of ##n## linearly independent vectors in an ##n-##dimensional vector space, ##V##, forms a basis for that space. Proof: Consider

$$
c_0 v+\sum_{i=1}^n c_i (v_i + e_i)
$$

with arbitrary ##c_i## ##(i= 0, 1, \dots , n)## and ##v \in V##. The equation

$$
c_0 v+\sum_{i=1}^n c_i (v_i + e_i) = 0 \qquad (*)
$$

cannot imply

$$
c_i = 0 \qquad i= 0, 1, \dots , n
$$

since that would mean that there are ##n+1## independent vectors in ##V##,

$$
\{ v , v_1 + e_1 , \dots , v_n + e_n \}
$$

and the dimension of ##V## would not be ##n##, but would be at least ##n+1##. Therefore, a set ##\{ c_i \}## exists, with at least two non-zero members, such that ##(*)## is satisfied. One cannot have ##c_0 = 0##, since that would lead to the conclusion that all the ##c_i##'s are zero. Therefore, we can write

$$
v = - \sum_{i=1}^n \frac{c_i}{c_0} (v_i +e_i) .
$$

Therefore, an arbitrary vector ##v## has been expressed as a linear combination of the vectors ##\{ v_1 +e_1 , \dots , v_n + e_n \}##. This proves, in addition to being linearly independent, that they span the space and hence are a basis.
 
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1. What is an inner product space?

An inner product space is a mathematical concept that describes a vector space with an additional structure. This structure is defined by an inner product, which is a function that takes in two vectors and produces a scalar value. This scalar value represents the "angle" between the two vectors and can also be thought of as a measure of their similarity.

2. What is a basis in an inner product space?

A basis in an inner product space is a set of vectors that can be used to represent any other vector in that space. These vectors are linearly independent, meaning that none of them can be expressed as a linear combination of the others. Additionally, the inner product of any two basis vectors is equal to 0, except for when the two vectors are the same.

3. What is a modified basis in an inner product space?

A modified basis in an inner product space is a basis that has been transformed using an orthogonal transformation. This transformation preserves the inner product of the basis vectors, meaning that the modified basis still spans the same space as the original basis. However, the modified basis may have different properties, such as being easier to work with or having a simpler structure.

4. How is a modified basis useful in an inner product space?

A modified basis can be useful in an inner product space for a variety of reasons. For example, it can simplify calculations and proofs by transforming the basis into a more convenient form. It can also provide insight into the structure of the space and allow for easier visualization of vectors and their relationships. Additionally, a modified basis can be used to solve problems and make predictions in various fields, such as physics and engineering.

5. Can a modified basis be used in any type of inner product space?

Yes, a modified basis can be used in any type of inner product space. This includes finite-dimensional spaces, infinite-dimensional spaces, and even non-Euclidean spaces. However, the specific transformations used to modify the basis may differ depending on the properties and structure of the space. Additionally, the modified basis may not always be unique, as there are often multiple ways to transform a basis while preserving its inner product.

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