I. Simon, J. Gill. Polynomial reducibilities and upward diagonalizations, Proc. 9th ACM Symposium on Theory of Computing, 1977, pp. 186--194. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Dot Operators - Borchert, Silvestri (Correct)
.... [16] k positive truth table [16] k GammaT (k Turing) tt (truth table [16] c (conjunctive [16] disjunctive [16] T (Turing) m (nondeterministic many one [16] c (nondeterministic conjunctive [16] xiii) T (nondeterministic Turing [16] xiv) T (PSPACE Turing [22]) b) The following reducibilities can be represented by promise dot operators but not by complementary dot operators: bounded truth table [16] strong nondeterministic Turing [17] random many one [1, 29] BPP many one [1, 29] c) The following reducibilities can be represented ....
I. Simon, J. Gill. Polynomial reducibilities and upward diagonalizations, Proc. 9th ACM Symposium on Theory of Computing, 1977, pp. 186--194.
Dot Operators - Borchert, Silvestri (Correct)
.... (k Turing) vii) p tt (truth table [16] viii) p c (conjunctive [16] ix) p d (disjunctive [16] x) p T (Turing) xi) NP m (nondeterministic many one [16] xii) NP c (nondeterministic conjunctive [16] xiii) NP T (nondeterministic Turing [16] xiv) PS T (PSPACE Turing [22]) b) The following reducibilities can be represented by promise dot operators but not by complementary dot operators: i) p btt (bounded truth table [16] ii) SN T (strong nondeterministic Turing [17] iii) RP m (random many one [1, 29] iv) BPP m (BPP many one [1, 29] c) The ....
I. Simon, J. Gill. Polynomial reducibilities and upward diagonalizations, Proc. 9th ACM Symposium on Theory of Computing, 1977, pp. 186--194.
Towards Efficient Monitoring - Jiao, Naqvi, Raz, Sugla (2000) (5 citations) (Correct)
....still on going, and it is out of the scope of this paper. For this paper, we can assume that the constraints are provided by the user without considering how they can be obtained. Competitive analysis of on line algorithms was used to address a somewhat similar problem of moving data by S. Kahan [5]. He gave provely optimal on line algorithms for a restricted family of functions, and for linear constrains. For the monitoring problem, as far as we know this is the first formal study of the problem, and many of our results are preliminary in nature. We are still carrying out on going work on ....
....to save resources when monitoring a real network. Another interesting question is the best adaptation of the competitive analysis techniques for cases where the off line algorithm may do without any cost. Instead of comparing to the obvious algorithm, as we did, one may try to expand the ideas of [5] and compare the monitoring cost to the cost of the best possible online algorithm for a specific history line. We expect that improvements to our methods can be made in the future, and the framework can be applied to many network management applications. Acknowledgments Shamim Naqvi thanks T. ....
Simon Kahan. A Model for Data in Motion. Proc. of the 23th ACM symposium on theory of computing, (STOC91), pp. 267--277, 1991.
Minimizing the Monitoring Cost in Network Management - Jiao, Naqvi, Raz, Sugla (2 citations) (Correct)
....still on going, and it is out of the scope of this paper. For this paper, we can assume that the constraints are provided by the user without considering how they can be obtained. Competitive analysis of on line algorithms was used to address a somewhat similar problem of moving data by S. Kahan [4]. He gave provably optimal on line algorithms for a restricted family of functions, and for linear constrains. For the monitoring problem, as far as we know this is the first formal study of the problem, and many of our results are preliminary in nature. We are still carrying out on going work on ....
....to save resources when monitoring a real network. Another interesting question is the best adaptation of the competitive analysis techniques for cases where the off line algorithm may do without any cost. Instead of comparing to the obvious algorithm, as we did, one may try to expand the ideas of [4] and compare the monitoring cost to the cost of the best possible online algorithm for a specific history line. We expect that improvements to our methods can be made in the future, and the framework can be applied to many network management applications. Acknowledgments Shamim Naqvi thanks T. ....
Simon Kahan, A Model for Data in Motion Proc. of the 23th ACM symposium on theory of computing, (STOC91), pp. 267--277, 1991.
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