Skip to main content

(10) Max determinant problem: Conclusion

Conclusion

I and some of my colleagues think about max determinant problem in this article. I don't know there is a better method than combination method. But this algorithm is sufficient to answer the Strang's question, case n=6. In the deep mathematics, people know the max determinant up to 100, at least. The method I show here has nothing compare to them, my method is so primitive. These mathematicians researched on this problem around 1960. I assume they even don't use a computer. I use today's computer, but, I can not have n = 10 case. How they know n = 100? I do like, ``I see the answer is in the one of 10 million, then, I can just write a program to find it.'' I feel having a computer makes me dull.

The following is the timing result on my computer (Core2 Duo P8400@2.26GHz, Linux 2.6.35, Windows Vista). Please note, Joachim's algorithm can reduce the n by 1. I measured Joachim's implementation with the time command on Linux, octave/matlab implementation with tic/toc command. $n<=5$ case, the elapsed time is too small and user time is 0. The ``()'' shows an estimated value.


This time, we implemented the same algorithm with octave, matlab, and C++. If we normalized the result with the number of the determinant computation, C++ implementation is 3.7 times faster than matlab implementation, 55 times faster then octave implementation. How you interpret this number is depends on your point of view. But I am impressed that the matlab implementation is only 3.7 times slower than C++.

Acknowledgements

Thanks to Leo, Joachim, and Marc for the discussion.

Comments

Popular posts from this blog

Why parallelogram area is |ad-bc|?

Here is my question. The area of parallelogram is the difference of these two rectangles (red rectangle - blue rectangle). This is not intuitive for me. If you also think it is not so intuitive, you might interested in my slides. I try to explain this for hight school students. Slides:  A bit intuitive (for me) explanation of area of parallelogram  (to my site, external link) . 

Geometric Multiplicity: eignvectors (2)

If eigenvectors of a matrix A are independent, it is a happy property. Because the matrix A can be diagonalized with a matrix S that column vectors are eigenvectors of A . For example, Why this is a happy property of A? Because I can find A's power easily. A^{10} is not a big deal. Because Λ is a diagonal matrix and power of a diagonal matrix is quite simple. A^{10} = SΛ^{10} S^{-1} Then, why if I want to compute power of A ? That is the same reason to find eigenvectors. Eigenvectors are a basis of a matrix. A matrix can be represented by a single scalar. I repeat this again. This is the happy point, a matrix becomes a scalar. What can be simpler than a scalar value. But, this is only possible when the matrix S's columns are independent. Because S^{-1} must be exist. Now I come back to my first question. Is the λ's multiplicity related with the number of eigenvectors? This time I found this has the name. Geometric multiplicity (GM): the number of in...

Tezuka Osamu's Black Jack, "Shrinking"

I like several novel authors. My first favorite author is probably Teduka, Osamu. I still love him. The list grows by adding Hoshi, Shinichi, Agatha Christie, Hermann Hesse, and so forth. My first favorite article of Tezuka was Atom as most of the (boy's) Tezuka fans did. But my favorite is Black Jack. I try to summarize one story, it is still quite vivid in my memory. I first read this story when I was 13 - 15 years old. I re-read it at least several times since Black Jack is composed of many short episodes. The title should be "ちぢむ (SHRINKING)" or it might be "縮む(Shrinking)". (It is not so convenient to translate this to English, since English does not have a system to say the exact same word in several ways. So I just simulate it with capital letters.) Black Jack is a genius surgeon, but he does not have the license. In short, his medical activity is illegal. His skill is top level in the world, but, the fee is also out-of-law expensive. In the story ...