Edgar Daylight was trained both as a computer scientist and as a historian. He writes a historical blog themed for his near-namesake Edsger Dijkstra, titled, “Dijkstra’s Rallying Cry for Generalization.” He is a co-author with Don Knuth of the 2014 book: *Algorithmic Barriers Failing: P=NP?*, which consists of a series of interviews of Knuth, extending their first book in 2013.

Today I wish to talk about this book, focusing on one aspect.

The book is essentially a conversation between Knuth and Daylight that ranges over Knuth’s many contributions and his many insights.

One of the most revealing discussions, in my opinion, is Knuth’s discussion of his view of asymptotic analysis. Let’s turn and look at that next.

## Asymptotic Analysis

We all know what asymptotic analysis is: Given an algorithm, determine how many operations the algorithm uses in worst case. For example, the naïve matrix product of square by matrices runs in time . Knuth dislikes the use of notation, which he thinks is used often to hide important information.

For example, the correct the count of operations for matrix product is actually

In general Knuth suggests that we determine, if possible, the number of operations as

where and are both explicit functions and is lower-order. The idea is that not only does this indicate more precisely that the number of operations is , not just , but also is forces us to give the exact constant hiding under the . If the constant is only approached as increases, perhaps the difference can be hidden inside the lower-order term.

An example from the book (page 29) is a discussion of Tony Hoare’s quicksort algorithm. Its running time is , on average. This allows one, as Knuth says, to throw all the details away, including the exact machine model. He goes on to say that he prefers to know:

that quicksort makes comparisons, on average, and exchanges, and stack adjustments, when sorting random numbers.

## The Novelty Game

Theorists create algorithms as one of their favorite activities. A classic way to get a paper accepted into a top conference is to say: In this paper we improve the running time of the best known algorithm for X from order to by applying methods Y.

But is the algorithm of this paper really *new*? One possibility is that the analysis of the previous paper was too coarse and the algorithms are actually the same. Or at least equivalent. The above information is logically insufficient to rule out this possibility.

Asymptotic analysis à-la Knuth comes to the rescue. Suppose that we proved that the older algorithm X ran in time

Then we would be able to conclude—without any doubt—that the new algorithm was indeed new. Knuth points this out in the interviews, and adds a comment about practice. Of course losing the logarithmic factor may not yield a better running time in practice, if the hidden constant in is huge. But whatever the constant is, the new algorithm must be new. It must contain some new idea.

This is quite a nice use of analysis of algorithms in my opinion. Knowing that an algorithm contains, for certain, some new idea, may lead to further insights. It may eventually even lead to an algorithm that is better both in theory and in practice.

## Open Problems

Daylight’s book is a delight—a pun? As always Knuth has lots to say, and lots of interesting insights. The one caveat about the book is the subtitle: “P=NP?” I wish Knuth had added more comments about this great problem. He does comment on the early history of the problem: for example, explaining how Dick Karp came down to Stanford to talk about his brilliant new paper, and other comments have been preserved in a “Twenty Questions” session from last May. Knuth also reminds us in the book that as reported in the January 1973 issue of *SIGACT News*, Manny Blum gave odds of 100:1 in a bet with Mike Paterson that P and NP are *not* equal.

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