Infinite-dimensional matrix multiplication

In summary, there is no general criterion for asociativity in infinite dimensional matrix multiplication, but it can be valid under certain conditions.
  • #1
alex5
2
0
We know that infinite-dimensional matrix multiplication in general isn't asociative. But, is there any criteria when asociativity is valid?
thanks in advance.
 
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  • #2
Well, isn't this the same thing as asking when you can interchange two summation symbols?
 
  • #3
Yes, I think you'r right. Can we say: we can interchange two summation symbols if both exists?
 
  • #4
Some buzzwords and a book

Relevant buzzwords include Tonelli theorem and Fubini theorem. In terms of doubly indexed infinite series, these basically say that absolute convergence is the key. See Bartle, The Elements of Real Analysis, 2nd Edition, Wiley, 1976, section 36, for an undergraduate level discussion.
 
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  • #5
I am puzzled. If multiplying infinite matrices corresponds to composing linear maps, as in the finite dimensional case, then it seems it would be associative, since composition is so.

I guess these matrices do not correspond to linear maps in the algebraic sense, as in that case there would be a finiteness condition on the number of non zero entries in the columns.
 
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  • #6
mathwonk said:
I am puzzled. If multiplying infinite matrices corresponds to composing linear maps, as in the finite dimensional case, then it seems it would be associative, since composition is so.

I guess these matrices do not correspond to linear maps in the algebraic sense, as in that case there would be a finiteness condition on the number of non zero entries in the columns.

Consider, for example:

[tex]a_{ij}=\frac{1}{2^{i+j}}[/tex]
[tex]b_{ij}=1[/tex]
[tex]c_{ij}=\frac{1}{i+j}[/tex]

Some products will be convergent, and some will be divergent.
 
  • #7
one needs to be a little more precise. in linear algebra, convergence is not an issue, only in analysis.

so one needs to say what subject one is working in, and what one means by "associative".

the matrices you gave do not represent maps in terms of a basis in the linear algebra sense.
 
  • #8
mathwonk said:
one needs to be a little more precise. in linear algebra, convergence is not an issue, only in analysis.

so one needs to say what subject one is working in, and what one means by "associative".

the matrices you gave do not represent maps in terms of a basis in the linear algebra sense.

I meant that the elements in of the matrix are divergent sums.
 
  • #9
mathwonk said:
(Is it perhaps Tonelli?)

Yes, indeed, thanks. I have made the correction.
 

1. What is infinite-dimensional matrix multiplication?

Infinite-dimensional matrix multiplication is a mathematical operation that involves multiplying two matrices where the number of rows and columns are infinite. This type of multiplication is commonly used in linear algebra and functional analysis.

2. What makes infinite-dimensional matrix multiplication different from regular matrix multiplication?

The main difference between infinite-dimensional matrix multiplication and regular matrix multiplication is that in infinite-dimensional matrix multiplication, the matrices have an infinite number of rows and columns, while in regular matrix multiplication, the number of rows and columns is finite.

3. What are some applications of infinite-dimensional matrix multiplication?

Infinite-dimensional matrix multiplication has many applications in mathematics, physics, and engineering. It can be used to solve differential equations, study linear transformations, and analyze complex systems.

4. How is infinite-dimensional matrix multiplication computed?

Infinite-dimensional matrix multiplication is computed using the same principles as regular matrix multiplication. However, since the matrices have an infinite number of rows and columns, the calculations may be more complex and involve techniques such as functional analysis and convergence.

5. Are there any limitations or challenges with infinite-dimensional matrix multiplication?

One of the main challenges with infinite-dimensional matrix multiplication is that it can be difficult to visualize and understand due to the infinite number of dimensions involved. Additionally, the calculations can become more complex and time-consuming, making it challenging to apply in certain real-world scenarios.

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