Determinate of a Matrix of any (square) order

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SUMMARY

The discussion focuses on calculating the determinant of an nXn matrix using Gaussian Elimination. The user seeks an efficient algorithm beyond the 2x2 and 3x3 matrices they are familiar with. They reference the triangular matrix property, which states that the determinant can be computed as the product of the diagonal entries when the matrix is in upper triangular form. The user concludes that implementing Gaussian Elimination will facilitate this process.

PREREQUISITES
  • Understanding of matrix operations and properties
  • Familiarity with Gaussian Elimination algorithm
  • Basic knowledge of triangular matrices
  • Programming skills in a language suitable for matrix manipulation
NEXT STEPS
  • Research the implementation of Gaussian Elimination in programming languages such as Python or C++
  • Study the properties of triangular matrices and their determinants
  • Explore numerical stability issues in matrix computations
  • Learn about alternative methods for determinant calculation, such as LU decomposition
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Mathematicians, computer scientists, and software developers involved in numerical analysis, matrix computations, or algorithm development will benefit from this discussion.

sciwizeh
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Hello, I'm new to this site.

I am trying to write a program that will deal with matrices, my problem is in finding a determinate, it would be easy if I limit the usage to 2x2 and 3x3, for which I already know the algorithms. I think that limiting the usage in that way would limit the usefulness of the program. Is there an "easy to program" algorithm for an nXn matrix? I saw something about getting it to upper triangle and multiplying the diagonal numbers together does this work for all matrices?
 
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I believe so: http://en.wikipedia.org/wiki/Determinant, you can find this quote:

If A is a triangular matrix, i.e. A_{i,j} = 0 \, whenever i > j or, alternatively, whenever i < j, then [tex]\det(A) = A_{1,1} A_{2,2} \cdots A_{n,n}[/tex] (the product of the diagonal entries of A).

But maybe you'll enjoy this paper http://www.axler.net/DwD.pdf :)
 
Thanks. That wikipedia article was kind of confusing, at least for me. OK that means I just need to make the program do Gaussian Elimination, which shouldn't be too hard, I hope.
 

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