By Karen A. Frenkel
Communications of the ACM,
February 1986,
Vol. 29 No. 2, Pages 110-111
10.1145/5657.214905
Comments
To illustrate the "remarkable extent to which complexity theory operates by means of analogs from computability theory," Richard Karp created this conceptual map or jigsaw puzzle. To lay out the puzzle in the plane, he used a "graph planarity algorithm." The more distantly placed parts might not at first seem related, "but in the end, the theory of NP-completeness does bring them all together," Karp says.
The upper right portion of the puzzle shows concepts related to combinatorial explosions and the notion of a "good" or "efficient" algorithm. In turn, "Complexity" connects these concepts to the upper left portion, which represents the concerns of early computability theorists.
The traveling salesman problem is closer to the upper right corner because it is probably intractable. It therefore borders on "NP-completeness" and "Combinatorial explosion."
To some extent, however, certain divisions blur. "Linear programming," for example, has an anomalous status—the most widely used algorithms for solving such problems in practice are not good in the theoretical sense, and those that are good in the theoretical sense are often not good in practice. One example is the ellipsoid method that was the object of so much attention six years ago. It ran in polynomial time, but the polynomial was of such a high degree that the method proved good in the technical sense, but ineffective in practice. "The reason is that our notion of polynomial-time algorithms doesn't exactly capture the notion of an intuitively efficient algorithm," Karp explains. "When you get up to n5 or n6, then it's hard to justify saying that it is really efficient. So Edmonds's concept of a good algorithm isn't quite a perfect formal counterpart of good in the intuitive sense." Further, the simplex algorithm is good in every practical sense, Karp says, but not good according to the standard paradigm of complexity theory. The most recent addition to linear programming solutions, an algorithm devised by Narendra Karmarkar that some think challenges the simplex algorithm, is good in the technical sense and also appears to be quite effective in practice, says Karp.
The good algorithm segment is adjacent to "Heuristics" because a heuristic algorithm may work well, but lack a theoretical pedigree. Some heuristic algorithms are always fast, but sometimes fail to give good solutions. Others always give an optimal solution, but are not guaranteed to be fast. The simplex algorithm is of the latter type.
"Undecidability, " "Combinatorial explosion," and "Complexity" are on the same plane because they are analogs of one another; undecidability involves unbounded search, whereas combinatorial explosions are by definition very long but not unbounded searches. Complexity theory bridges the gap because, instead of asking whether a problem can be solved at all, it poses questions about the resources needed to solve a problem.
The lower left region contains the segments Karp has been concerned with most recently and that contain open-ended questions. "Randomized algorithm," for example, is situated opposite "Probabilistic analysis" because both are alternatives to worst-case analyses of deterministic algorithms. Randomized algorithms might be able to solve problems in polynomial time that deterministic ones cannot and that could mean an extension of the notion of good algorithms. Perhaps through software designs for non-von Neumann machines, algorithms can be made more efficient in practice through parallelism. Finally, some parts of the puzzle are not yet defined. Says Karp, "They correspond to the unknown territory that remains to be explored in the future."
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