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All known NP-complete sets are P-isomorphic (i.e. there are polynomial time, one-to-one and onto, polynomial time invertible reductions between any two known NP-complete sets) [BH]. If all NP-complete sets are P-isomorphic, then. P ≠ NP. However it is not known if the existence of more than one P-isomorphism class of NP-complete sets has implications for the P = NP? problem. In the main result of...
We study a restricted programming language over tree structures. For this language, we give systematic translation rules which map programs into complexity descriptors. The descriptors are in the form of generating functions of average costs. Such a direct approach avoids the recourse to recurrences; it therefore simplifies the task of analyzing algorithms in the class considered and permits analysis...
Two different methods of flow analysis are discussed, one a significant generalization of the other. It is shown that the two methods have significantly different intrinsic computational complexities. As an outgrowth of our observations it is shown that a feature of the programming language used by Dijkstra in A Discipline of Programming makes it unsuitable for compile-time type checking, thus suggesting...
Traditional implementations of conditional critical regions and monitors can lead to unproductive "busy waiting" if processes are allowed to wait on arbitrary boolean expressions. Techniques from global flow analysis may be employed at compile time to obtain information about which critical regions (monitor calls) are enabled by the execution of a given critical region (monitor call). We...
Fully polynomial approximation algorithms for knapsack problems are presented. These algorithms are based on ideas of Ibarra and Kim, with modifications which yield better time and space bounds, and also tend to improve the practicality of the procedures. Among the principal improvements are the introduction of a more efficient method of scaling and the use of a median-finding routine to eliminate...
A combinatorial problem related to storage allocation is analyzed. The problem falls into a class of NP-complete, one-dimensional bin-packing problems. We propose an iterative approximation algorithm and show that it is superior to an earlier heuristic presented for this problem. The bulk of the paper is devoted to the proof of a worst-case performance bound.
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