-= 10 Technical Papers Every Programmer Should Read (At Least Twice) =- 10 Technical Papers Every Programmer Should Read (At Least Twice) this is the second entry in a series on programmer enrichment Inspired by a fabulous post by Michael Feathers along a similar vein, I’ve composed this post as a sequel to the original. That is, while I agree almost wholly with Mr. Feather’s1 choices, I tend to think that his choices are design-oriented2 and/or philosophical. In no way, do I disparage that approach, instead I think that there is room for another list that is more technical in nature, but the question remains, where to go next? In this post I will offer some guidance based on my own readings. All papers are freely available online (i.e. not pay-walled)They are technical (at times highly so)They cover a wide-range of topicsThe form the basis of knowledge that every great programmer should know, and may already Because of these constraints I will have missed some great papers, but for the most part I think this list is solid.
A Visionary Flood of Alcohol by C. Best paper awards for AAAI, ACL, CHI, CIKM, FOCS, ICML, IJCAI, KDD, OSDI, SIGIR, SIGMOD, SOSP, STOC, UIST, VLDB, WWW. Guru of the Week (GotW) Archive - Main Index Page. Complexity Cases in Wolfram. Posted by An algorithm is, in essence, a procedure given by a finite description that solves some computational problem. The field of computational complexity deals with questions of the efficiency of algorithms, i.e. “For a computational problem X, how many steps does the best algorithm perform in solving X?”
You might think that questions in this field would be confined to the realm of computer science, except for the fact that computational complexity theory contains the mathematical problem of the century! Currently, many mathematicians around the world are attempting to solve the famed open problem P vs. NP, a problem so important that it is one of the seven millennium problems of the Clay Mathematics Institute and carries a million dollar prize. In fact, according to our logs, many of you tried to ask Wolfram|Alpha this same question before this new functionality was available! No discussion of complexity theory would be complete—no pun intended—without touching on the P vs. How We Made GitHub Fast - GitHub. Bartholdi on spacefilling curves. Figure 1: A heuristic solution to the Traveling Salesman Problem is to visit the points in the same sequence as the Sierpinski spacefilling curve.
A spacefilling curve is a continuous mapping from a lower-dimensional space into a higher-dimensional one. A famous spacefilling curve is that due to Sierpinski, which is formed by repeatedly copying and shrinking a simple pattern (the convoluted tour in Figure 1). A useful property of a spacefilling curve is that it tends to visit all the points in a region once it has entered that region. Thus points that are close together in the plane will tend to be close together in appearance along the curve. This forms the basis of the heuristic, invented by L. Platzman and me, to produce a reasonably short tour of n given locations (the so-called Travelling Salesman's Tour): Simply visit them in the same sequence as does the spacefilling curve. The spacefilling curve heuristic has been used in many applications, including: