| Mark Jerrum, Leslie Valiant, and Vijay Vazirani. Random Generation of Combinatorial Structures from a Uniform Distribution. Theoretical Computer Science, 43, pages 169--188, 1986. |
.... Delta(A) within error ffl we have to average O(nffl ) values dist(x i ; A) By doing that, we allow probability 0.1 of failure. As usual, to attain a lower probability ffi 0 of failure, one should run Algorithm 3. 3 O(ln ffi ) times and then select the median of the computed ff s (cf. Jerrum et al. 86] For all applications, choosing ffl = 1 will suffice and in many cases ffl = n will do (cf. Section 5.1) Hence, often we will have to apply Oracle 2.2 only a constant number of times. We would like to relate the value of Delta(A) to the cardinality jAj. 3.7) Definition. Entropy ....
M. Jerrum, L.G. Valiant and V.V. Vazirani, Random generation of combinatorial structures from a uniform distribution, Theoret. Comput. Sci., 43 (1986), no. 2-3, 169--188.
....at least 3 4 (1 #) # 4) 1 X (1 # 4) #) # , 4) completing the proof of the first part of the theorem. Making the approximation with probability # for any # 0 is done by rerunning O(ln 1 #) times the procedure for estimating X and taking the median of the results [15]. It is also possible to extend Theorem 2 to the case where W (# 1 ) cannot be exactly computed, but can be accurately estimated. Corollary 3 Assume a b for all i. Let the sample size S = #, W (# 1 ) s estimate be within # 2 of its true value with probability 3 4, and ....
M. R. Jerrum, L. G. Valiant, and V. V. Vazirani. Random generation of combinatorial structures from a uniform distribution. Theoretical Computer Science, 43:169--188, 1986.
....of S by one during each stage, the cardinality of S is bounded by a polynomial. 2 We also note that the above process can be implemented by a probabilistic polynomial time bounded machine that uses SAT as an oracle. At any stage, we can pick circuits from T i in an approximately uniform manner [JVV86]. 4 Application to Two Queries In this section we show an application of our lemma to the two queries problem. Theorem 4.1 If P , then PH = S 2 . To prove Theorem 4.1 we need the following theorem by Buhrman and Fortnow [BF99] Theorem 4.2 (Buhrman Fortnow) If P then there exists a ....
M. Jerrum, L. Valiant, and V. Vazirani. Random generation of combinatorial structures from a uniform distribution. Theoretical Computer Science, 43(1986), pp. 169--188.
....the size of output. Observe that for the listing problem it is necessary to include the output in the running time estimation. For further details and examples of generalized model checking the reader is referred to [15] For more general background on this taxonomy of combinatorial problems, see [16, 17]. Although our algorithms depend heavily on the notion of tree width, it is not necessary to introduce it formally. The interested reader is referred to [ 5, 10] Intuitively, the tree width measures the similarity of a structure with a tree. For instance, a tree has tree width tw(T ) 1 and a ....
M. Jerrum, L. Valiant, and V. Vazirani. Random generation of combinatorial structures from a uniform distribution. Theoretical Computer Science, 43:169--188, 1986.
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Mark Jerrum, Leslie Valiant and Vijay Vazirani. Random generation of combinatorial structures from a uniform distribution. Theoretical Computer Science, 43:169--188, 1986.
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Mark Jerrum, Leslie Valiant and Vijay Vazirani, Random generation of combinatorial structures from a uniform distribution, Theoretical Computer Science 43 (1986), 169--188.
.... algorithm that takes as input a group G and 0, and produces as output a number Y (a random variable) such that Pr (1 Gamma )P G (k; k) Y (1 )P G (k; k) and, moreover, does so within time poly(n; b) Is there a polynomial time almost uniform sampler [16] for the orbits of Sigma under the action of G That is to say, is there a randomised algorithm that takes as input a group G and 0, and produces as output a word Y 2 Sigma (a random variable) such that for each orbit O, 1 Gamma )N Pr(Y 2 O) 1 )N where N = PG (k; ....
....be closely related, which would lead one to suppose that questions When this concept was first introduced, generator was used in place of sampler , but the latter word is more specific. A rather precise statement of this relationship has been formulated by Jerrum, Valiant, and Vazirani [16]. a) b) and (c) ought to be equivalent. However, the situation here is atypical, and it is not clear, for example, whether resolving question (a) in the affirmative would immediately settle either of the others. The two known entailments are described in the following proposition, whose ....
M. R. Jerrum, L. G. Valiant and V. V. Vazirani, Random generation of combinatorial structures from a uniform distribution, Theoretical Computer Science 43 (1986), pp. 169--188.
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Mark Jerrum, Leslie Valiant, and Vijay Vazirani. Random Generation of Combinatorial Structures from a Uniform Distribution. Theoretical Computer Science, 43, pages 169--188, 1986.
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M. Jerrum, L. G. Valiant, and V. V. Vazirani. Random generation of combinatorial structures from a uniform distribution. Theor. Comput. Sci., 43:169--188, 1986.
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M. R. Jerrum, L. G. Valiant, and V. V. Vazirani. Random generation of combinatorial structures from a uniform distribution. Theor. Comput. Sci., 43:169--188, 1986.
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M. Jerrum, L. Valiant, and V. Vazirani. Random generation of combinatorial structures from a uniform distribution. Theoretical Computer Science, pages 169--188, 1986.
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M. Jerrum, L. G. Valiant, and V. V. Vazirani, Random generation of combinatorial structures from a uniform distribution, Theoretical Computer Science 43 (1986), 169--188.
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M. Jerrum, L. Valiant, and V. Vazirani. Random generation of combinatorial structures from a uniform distribution. Theoretical Computer Science, 43:169--188, 1986.
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M. R. Jerrum, L. G. Valiant, and V. V. Vazirani. Random generation of combinatorial structures from a uniform distribution. Theoretical Comput. Sci., 43(2-3):169--188, 1986.
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M. Jerrum, L. Valiant and V. Vazirani. Random Generation of Combinatorial Structures from a Uniform Distribution. Theoretical Computer Science, Vol. 43, pp. 169--188, 1986.
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M.R. Jerrum, L.G. Valiant and V.V. Vazirani, Random generation of combinatorial structures from a uniform distribution, Theoretical Computer Science 43 (1986), 169-188.
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Jerrum, M., Valiant, L., and Vazirani, V. (1986). Random generation of combinatorial structures from a uniform distribution. Theoretical Computer Science, 43:169--188.
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M. Jerrum, L. Valiant, and V.V. Vazirani. Random generation of combinatorial structures from a uniform distribution. Theoretical Computer Science 43:169-188 (1986).
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M. Jerrum, L. Valiant and V. Vazirani, "Random Generation of Combinatorial Structures from a Uniform Distribution," Theoretical Computer Science 43 (1986), pp. 169-188.
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Mark Jerrum, Leslie G. Valiant, and Vijay V. Vazirani. Random generation of combinatorial structures from a uniform distribution. Theoretical Computer Science, 43:169--188, 1986.
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M. Jerrum, L. Valiant, and V. Vazirani. Random generation of combinatorial structures from a uniform distribution. Theoretical Computer Science, 43:169--188, 1986.
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M.R. Jerrum, L.G. Valiant and V.V. Vazirani, Random generation of combinatorial structures from a uniform distribution, Theoretical Computer Science 43(2-3):169-188, 1986.
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M. R. Jerrum, L. G. Valiant, and V. V. Vazirani. Random generation of combinatorial structures from a uniform distribution. Theoretical Computer Science, 43(2-3):169{ 188, 1986.
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Jerrum, M.R., Valiant, L.G. and Vazirani, V.V.: Random generation of combinatorial structures from a uniform distribution. Theoretical Computer Science, vol. 43, 1986, pp. 169-188.
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M.R. Jerrum, L.G. Valiant, and V.V. Vazirani, Random generation of combinatorial structures from a uniform distribution, Theoretical Computer Science, 43 (1986) 169-188.
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