We propose a new model of restricted branching programs specific to solving GEN problems, which we call incremental branching programs. We show that syntactic incremental branching programs capture previously studied models of computation for the problem GEN, namely marking machines
(Cook, S.A. in J. Comput. Syst. Sci. 9(3):308–316, 1974) and Poon’s extension (Proc. of the 34th IEEE Symp. on the Foundations of Computer Science, pp. 218–227, 1993) of jumping automata on graphs (Cook, S.A., Rackoff, C.W. in SIAM J. Comput. 9:636–652, 1980). We then prove exponential size lower bounds for our syntactic incremental model, and for some other variants of branching
program computation for GEN. We further show that nondeterministic syntactic incremental branching programs are provably stronger
than their deterministic counterpart when solving a natural NL-complete GEN sub-problem. It remains open if syntactic incremental
branching programs are as powerful as unrestricted branching programs for GEN problems.
A preliminary version of this paper appears as (Gál, A., Koucky, M., McKenzie, P., Incremental branching programs, in Proc.
of the 2006 Computer Science in Russia Conference CSR06. Lecture Notes in Computer Science, vol. 3967, pp. 178–190, 2006).
A. Gal supported in part by NSF Grant CCF-0430695 and an Alfred P. Sloan Research Fellowship. M. Koucky did part of this work
while being a postdoctoral fellow at McGill University, Canada and at CWI, Amsterdam, Netherlands. Supported in part by NWO
vici project 2004–2009, project No. 1M0021620808 of MŠMT ČR, grants 201/07/P276, 201/05/0124 of GA ČR, and Institutional Research
Plan No. AV0Z10190503.
P. McKenzie supported by the NSERC of Canada and the (Québec) FQRNT. 相似文献
Accurate knowledge of low-frequency electromechanical modes in power systems gives vital information about the stability of the system. Current techniques for estimating electromechanical modes are computationally intensive and rely on complex system models. This research complements model-based approaches and uses measurement-based techniques. This paper discusses the development of an autoregressive moving average (ARMA) block-processing technique to estimate these low-frequency electromechanical modes from measured ambient power system data without requiring a disturbance. This technique is applied to simulated data containing a stationary low-frequency mode generated from a 19-machine test model. The frequency and damping factor of the estimated modes are compared with the actual modes for various block sizes. This technique is also applied to 35-min blocks of actual ambient power system data before and after a disturbance and compared to results from Prony analysis on the ringdown from the disturbance. 相似文献
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