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Computing complexity distances between algorithms. (English) Zbl 1249.54069

Summary: We introduce a new (extended) quasi-metric on the so-called dual \(p\)-complexity space, which is suitable to give a quantitative measure of the improvement in complexity obtained when a complexity function is replaced by a more efficient complexity function on all inputs, and show that this distance function has the advantage of possessing rich topological and quasi-metric properties. In particular, its induced topology is Hausdorff and completely regular.
Our approach is applied to the measurement of distances between infinite words over the decimal alphabet and some advantages of our computations with respect to the ones that provide the classical Baire metric are discussed.
Finally, we show that the application of fixed point methods to the complexity analysis of Divide and Conquer algorithms, presented by M. Schellekens [in: S. Brookes (ed.) et al., Mathematical foundations of programming semantics. Proceedings of the 11th conference (MFPS), Tulane Univ., New Orleans. Amsterdam: Elsevier, Electronic Notes in Theoretical Computer Science. 1, 22 p. (1995; Zbl 0910.68135)], can be also given from our approach.

MSC:

54E50 Complete metric spaces
54H25 Fixed-point and coincidence theorems (topological aspects)
54H99 Connections of general topology with other structures, applications
68Q25 Analysis of algorithms and problem complexity

Citations:

Zbl 0910.68135
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References:

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