Efficient Methods for Computing Determinants of Large Matrices

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Discussion Overview

The discussion centers on methods for computing determinants of large matrices, exploring various computational techniques and their efficiency. Participants consider both theoretical and practical aspects of determinant calculation, including algorithmic complexity and numerical stability.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant questions the efficiency of the cofactor method for large matrices, suggesting it may be too time-consuming.
  • Another participant proposes that row operations could be a viable method, estimating the computational complexity to be approximately order n^4 for a naive approach.
  • A different viewpoint raises concerns about the computational feasibility of a summation-based determinant formula, questioning its efficiency for algorithmic implementation.
  • One participant calculates that picking out distinct terms in the determinant summation could lead to a factorial growth in operations, indicating potential inefficiency.
  • Reference is made to MATLAB's use of LU decomposition for determinant calculation, with singular value decomposition suggested for ill-conditioned matrices, highlighting established computational practices.
  • Another participant reflects on their own method as potentially the "best worst method," acknowledging the need for clever approaches in practical scenarios involving small matrix entries.
  • A question is raised about the efficiency of row-reduction methods compared to other techniques, indicating uncertainty about the time required for such operations.

Areas of Agreement / Disagreement

Participants express a range of views on the efficiency of different methods for computing determinants, with no consensus reached on the best approach. Several competing methods and concerns about computational complexity are discussed.

Contextual Notes

Participants mention various assumptions regarding the size and nature of matrices, as well as the potential impact of numerical stability on determinant calculations. Specific computational steps and their implications remain unresolved.

Who May Find This Useful

This discussion may be of interest to those involved in numerical analysis, computer science, or applied mathematics, particularly in contexts requiring efficient matrix computations.

amcavoy
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How do computers evaluate determinants of large matrices? The cofactor method seems like it would be too time consuming. Does anyone know?
 
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my guess would be row operations. even a naive method takes n-1 operations with n and multipkciations additions for the first column, n-2 operations with n-1 additions so approximately order n^4 operations to work out a diagonal form, then multiply the n thingst together, not much more work...so quite cheap really, and that's just the naive version.
 
How would this fare computationally?

[tex]det(A) = \sum_{{i}_1, {i}_2,...,{i}_n = 1}^{N} \epsilon_{{i}_1, {i}_2,...,{i}_n} a_{{i}_1, 1} \cdot a_{{i}_2, 2} \cdot \cdot \cdot a_{{i}_n, N}[/tex]

Cumbersome and inefficient for a computer algorithm?
 
Well, how much work is it? You do n multiplications how many times?
 
Looks like if you pick out all the distinct terms (the ones in the summation that aren't equal to zero) you get (N!). On top of that, you would need to do N multiplications each time (so N multiplications N! times). Yikes

Yeah didn't work that one out before
 
I would think there are indeed many better ways than mine to find determinants. i would say mine is the best worst method or thee worst best method: the least complicated way of doing it that isn't naive. I've been told of other methods for speeding it up though i cant' recall them. of course when we think of a matrix we often think of something small with bige entries (ie integers) frequetly we need a clever method of finding it in the real world since we often have small entries, ie (numbers ivolved in the computation of) determinants so small that it may appear singular when it isn't.
 
So row-reduction (to get 0-entries) would be more efficient? Or would that take a long time in itself?
 

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