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alex5
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We know that infinite-dimensional matrix multiplication in general isn't asociative. But, is there any criteria when asociativity is valid?
thanks in advance.
thanks in advance.
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.
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.
mathwonk said:(Is it perhaps Tonelli?)
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.
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.
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.
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.
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.