Approximation Algorithms for NP-Hard Problems by Dorit Hochbaum

Approximation Algorithms for NP-Hard Problems Dorit Hochbaum ebook
Format: djvu
ISBN: 0534949681, 9780534949686
Page: 620
Publisher: Course Technology

I'm enjoying reading notes from Shuchi Chawla's course at the University of Wisconsin, Madison on approximation algorithms for NP-hard optimization problems. Pricing such an instrument involves solving an NP-complete problem, but no one would argue that this implies anything about real financial instruments. There are already arbitrarily good polynomial-time approximation algorithms for many NPO-complete problems like TSP, but TSP is actually APX-complete too, meaning you cannot even approximate answers beyond a certain factor unless P=NP. I still maintain that someone could make a good movie with the premise "random guy finds easy algorithm to solve NP-complete problems now what?" in the vein of Primer (random guys . It seems like there would be incentive to participate if players believed their approximation algorithm or embedding scheme could beat the next firm's. Abraham Flaxman | October 16, 2009 at 3:25 pm | Permalink. An infinitesimal advance in the traveling salesman problem breathes new life into the search for improved approximate solutions. The authors give another, similarly artificial, example: Consider for example a . In spite of their theoretical hardness, heuristic solvers are often able to solve large realistic problem instances surprisingly fast. Heuristics for NP-hard problems. The computer scientist Richard Karp, of the University of California at Berkeley, showed that the traveling salesman problem is “NP-hard,” which means that it has no efficient algorithm (unless a famous conjecture called P=NP is true — but the majority of computer scientists now suspect that it is false).