What Are the Practical Applications of Matrix Division?

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Matrix division can be expressed as either A/B or B\A, both of which can be computed without directly finding the inverse of B, provided B is not singular. The equations X = A/B and X = B\A can be solved using Gaussian elimination, yielding unique solutions if B is non-singular. In cases where B is singular, the pseudo-inverse must be used, often calculated through singular value decomposition. Practical applications of matrix division include solving systems of linear equations, optimization problems, and data fitting in various fields such as statistics and machine learning. Understanding these methods enhances the ability to work with complex matrix operations in real-world scenarios.
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Is it possible to compute matrix (A/B) without first finding the inverse of matrix B but ending with EITHER { A * (Inverse of B) } OR { (Inverse of B * A }...i think i discovered the trick
 
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Another way to write ##X=A/B \equiv AB^{-1}## is ##XB=A##. This has a unique solution X if B is not singular. You can solve for X in XB=A using Gaussian elimination.

Another way to write ##X=B \backslash A \equiv B^{-1}A## is ##BX=A##. This, too, has a a unique solution X if B is not singular. You can solve for X in BX=A using Gaussian elimination.

What if B is singular? The standard approach is to use the pseudo-inverse, and now you have but no choice to compute that inverse, typically via singular value decomposition.
 
what would be the uses of matrix division?
 
Here is a little puzzle from the book 100 Geometric Games by Pierre Berloquin. The side of a small square is one meter long and the side of a larger square one and a half meters long. One vertex of the large square is at the center of the small square. The side of the large square cuts two sides of the small square into one- third parts and two-thirds parts. What is the area where the squares overlap?

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