Abstract
Publisher Summary The computational complexity of a problem is a measure of the computational resources, typically time, required to solve the problem. The computational complexity of a problem should not be confused with the time used by a particular algorithm for the problem. However, the analysis of a particular algorithm can provide a useful upper bound on a problem's complexity. The term “inherent complexity” is sometimes used to emphasize that the complexity of a problem is a property of the problem itself, rather than a property of a particular algorithm. This chapter discusses computational complexity of NP-complete problems. This class of problems includes the traveling salesman problem, the Chinese postman problem, the vertex cover problem, and the edge cover problem. The chapter also discusses the class of problems having polynomial-time algorithms. This class is viewed as a paradigm for the class of “tractable” problems. Finally the chapter concludes with a discussion of an emerging theory of parallel computation.
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