Is the Proof for a Complex Inner Product Space Correct?

In summary, this proof is saying that if a scalar product f(u,v) isdefined, and the determinant of the matrix A is positive, then f(u,v) must also be positive.
  • #1
hsazerty2
2
1
Summary:: Inner Product Spaces, Orthogonality.

Hi there,
This my first thread on this forum :)

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I encountered the above problem in Schaum’s Outlines of Linear Algebra 6th Ed (2017, McGraw-Hill) Chapter 7 - Inner Product Spaces, Orthogonality.
Using some particular values for u and v, I proved that a and d must be real positive, and b is the conjugate of c. The solution indicates that a.d-b.c must also be positive, but i can't figure that out.

thanks for your help.

[Moderator's note: Moved from a technical forum and thus no template.]
 
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  • #2
---------------
Definition (Inner product) Let V be a vector space over IR.
An inner product ( , ) is a function V × V → IR with the following
properties
1. ∀ u ∈ V , (u, u) ≥ 0, and (u, u) = 0 ⇔ u = 0;
2. ∀ u, v ∈ V , holds (u, v) = (v, u);
3. ∀ u, v, w ∈ V , and ∀ a, b ∈ IR holds
(au + bv, w) = a(u, w) + b(v, w).
------------
Your estimate meets above 1. How about 2. and 3. ?

EDIT
As pointed out in #3 for conjugate inner product
2. (u,v)=(v,u)*
 
Last edited:
  • #3
anuttarasammyak said:
---------------
Definition (Inner product) Let V be a vector space over IR.
An inner product ( , ) is a function V × V → IR with the following
properties
1. ∀ u ∈ V , (u, u) ≥ 0, and (u, u) = 0 ⇔ u = 0;
2. ∀ u, v ∈ V , holds (u, v) = (v, u);
3. ∀ u, v, w ∈ V , and ∀ a, b ∈ IR holds
(au + bv, w) = a(u, w) + b(v, w).
------------
Your estimate meets above 1. How about 2. and 3. ?
We're dealing with a complex inner product here.
 
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  • #4
first check the condition ##(u,v)=\overline{(v,u)}##

Theorem. A function ##f(u,v)=u^TA\overline v,\quad A=(\overline {A})^T,\quad u,v\in\mathbb{C}^n## is positive definite iff the characteristic polynomial of the matrix ##A## has the form
$$\sum_{k=0}^n a_k\lambda^k,\quad a_k\in\mathbb{R},\quad a_k\ne 0,\quad\mathrm{sgn}\, a_{k}=-\mathrm{sgn}\,a_{k+1}$$
 
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  • #5
hsazerty2 said:
Summary:: Inner Product Spaces, Orthogonality.

Hi there,
This my first thread on this forum :)

[Moderator's note: Moved from a technical forum and thus no template.]
Hello, @hsazerty2 !
:welcome:
 
  • #6
@wrobel, Thanks for the reply, but i need to find a solution that does not involve the notion of characteristic polynomial or eigenvalues.
Is the below proof right ?

if f(u,v) is a scalar product, then f(u,u) must be positive for all u in C2, which means that the above matrix A must be positive definite, and for that its determinant should be positive. and i already proved that the above matrix is hermitian. So all the conditions are : a and d real positive, b is the conjugate of c, and the determinant is positive. Conversely, if all the above conditions are satisfied, then the matrix is hermitian and definite positive, so f(u,v) is a scalar product.
Right ?
 

1. What is a complex inner product space?

A complex inner product space is a vector space where the elements are complex numbers and there is a defined inner product operation, which is a generalization of the dot product in real vector spaces. This inner product operation satisfies certain properties, such as conjugate symmetry and linearity, and allows for the definition of concepts like length and angle in the complex vector space.

2. How is a complex inner product space different from a real inner product space?

While both complex and real inner product spaces have an inner product operation, the main difference is that in a complex inner product space, the inner product can take on complex values, whereas in a real inner product space, the inner product is always a real number. Additionally, the concept of orthogonality in a complex inner product space is more nuanced, as two vectors can be orthogonal even if their inner product is not equal to zero.

3. What is the significance of the inner product in a complex inner product space?

The inner product in a complex inner product space allows for the definition of important concepts such as norm, distance, and angle. It also enables the use of techniques like Gram-Schmidt orthogonalization and the projection theorem, which are essential in many areas of mathematics and physics.

4. Can a complex inner product space be infinite-dimensional?

Yes, a complex inner product space can be infinite-dimensional, just like a real inner product space. In fact, many important spaces in mathematics, such as Hilbert spaces and function spaces, are infinite-dimensional complex inner product spaces.

5. How is a complex inner product space used in applications?

Complex inner product spaces have a wide range of applications in mathematics, physics, and engineering. They are used in quantum mechanics, signal processing, and control theory, among others. In these applications, the inner product allows for the analysis and manipulation of complex-valued signals and systems, leading to important insights and practical solutions.

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