On March 1 2014 a paper of mine titled as above will be published in Advances in Linear Algebra and Matrix Theory Vol. 4 Number 1. This will be a open publishing venue. I am new to this kind as publishing experience, and I will provide here support, considerations, and opinions. I will post a commentary series to help the distribution and to explain the contents.

The title is to poke fun to the subject. In so far, the numerical analysis of these algorithms has been stale in the sense that has not been used to design more accurate algorithms: the analysis has been used to describe the worst case error for FastMMW but has not been used neither to design better algorithms nor to select the best algorithm for the input at hand. This point is embarrassingly important.

Let’s have a thought experiment about the sort algorithm, this will be appealing to CS people and Googler’s interviewers. There are about three dozens sorting algorithms with different best, average, and worst case scenarios. This seems like we have a lot of algorithms to choose from. For example, the merge sort and the quick sort have the same best and average performance and the Quick sort has the worst case performance of O(n^2). That is, every time we choose the pivot element to split the vector into two and sort recursively, we choose always the smallest or the largest for n consecutive times. Albert King would say that he would not know luck at all if it was not for bad luck or the vector is constant. Would you reject the quick sort a priori for that?

The community did not: the Quick sort comes with multiple pivots variations, with hierarchical algorithms to improve the overall performance: we choose the pivot and the number of recursions a finite number of times for the specific input, etc. For Sorting the attitude is different, the variety and number of algorithms is exciting, the combination of different algorithms to fit a specific purpose is elegant and it makes a great subject for job interviews (usually not very deep after all you need to know three dozens algorithms and you cannot use Google while interviewing at Google).

FastMMW have thousands of variations without considering the even faster ones. I am not kidding during a personal conversation with smarter people, one counted them in the Ks (thousands). The Bounds are for the family of algorithms (i.e., all sorting) not for each implementations, the bounds provide only worst case scenarios (forget about best and average), and there is no consideration about the inputs and their nature.

I understand we are talking about precision and not performance, we must be cautious. However, it is true that if we apply Strassen 2 times recursively, the error increases by a bounded constant. It is like to choose to use the quick sort for two level of recursions and then yield to merge sort.

In this work, The Better Accuracy of Strassen-Winograd Matrix Multiply I show that we can design more accurate algorithm even if we use a finite number of recursions, that the theory started 40 years ago is a great starting point if used in combination with new tools and ideas, especially new ideas.

The format of this series is still in my head and thus … but I will start with a new idea.