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Table 17: 100,000 Node Delaunay Graphs with Random Edge Weights (IBM 590, seconds)

in Computing Minimum-Weight Perfect Matchings
by William Cook, André Rohe 1999
"... In PAGE 16: ... To give a comparison with non-geometric instances, in Table 16 we report times on random Delanuay graphs where the integer edge weights are chosen at random (uniformly) from the interval 0-9,999. In Table17 , we give an indication of the... ..."
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Table 7: A comparison of cut-weight results for 50,000 graph variants with random edge weights, BVBF CA against the evolutionary search algorithm, BVBF BX, both with 3% imbalance tolerance

in A Combined Evolutionary Search and Multilevel Optimisation Approach to Graph Partitioning
by A. J. Soper, C. Walshaw, M. Cross 2000
"... In PAGE 14: ...Table 7: A comparison of cut-weight results for 50,000 graph variants with random edge weights, BVBF CA against the evolutionary search algorithm, BVBF BX, both with 3% imbalance tolerance Table7 shows a comparison of the graphs with random edge weights against the evolutionary algo-... ..."
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Table 2: This table reports how many offset-less encodings A, B, C, and D have two/multiple valid decodings for the same set of one million encodings of random triangulations with n vertices that start at a random edge and have at least one split.

in On the non-redundancy of split offsets in degree coding
by Martin Isenburg, Jack Snoeyink
"... In PAGE 8: ...Table2 we list how many offset-less encodings become non-unique because they have two/multiple de- codings as we omit more and more split information.... ..."
Cited by 1

Table 1: Maximum fraction of bits correctable for each of 100 error patterns, using probabilistic decoding. In each ex- periment, the edge-vertex graph of an algebraic expander was compared against random edge-vertex and bipartite graphs of equivalent size.

in Probabilistic Decoding of Low-Density Cayley Codes
by Adam L. Berger , John D. Lafferty
"... In PAGE 4: ... To address this question, we carried out some preliminary experiments comparing the random and algebraic constructions of (2; d)-regular graphs with parity-check subcodes. Table1 presents some representa- tive results from these experiments.... ..."

Table 3.1: Proved lower bounds on the average-case running times of some label-correcting algorithms on difficult input classes with D1 BP C7B4D2B5 edges and random edge weights.

in Design and Analysis of Sequential . . .
by Ulrich Meyer

Table 4: The worst and the average deviations from the optimum for different values of n (the number of vertices in a graph) [19].

in Minimizing maximal regret in discrete optimization problems with interval data
by Adam Kasperski, et al.
"... In PAGE 16: ... The results of the experiments are presented in Table 4. As it can be seen in Table4 , algorithm AMU performs much bet- ter than AU and AM separately. For the considered type of graphs with random edge costs, algorithm AMU is always within very few percentage points off the optimum.... ..."

Table 4. Average BDeu scores. (D7 BP BG, mfss BP BG; BEBHBCBN BCBCBC random edges considered for hillclimbing) dataset rand hlclmb CBBUC6CB CBBUC6CB+MIe CBBUC6CB+MIe+2nd CBBUC6CB+MIe+2nd+hlclmb

in Tractable Learning of Large Bayes Net Structures from Sparse Data
by Anna Goldenberg 2004
"... In PAGE 5: ...We tested our algorithm in a variety of configurations on the datasets listed in Table 3. The results in Table4 are reported in terms of the average BDeu score, i.e.... ..."
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Table 5. Number of links in the resulting nets. (D7 BP BG, mfss BP BG; BDBCBCBN BCBCBC random edges considered for hillclimbing) dataset rand hlclmb CBBUC6CB CBBUC6CB+MIe CBBUC6CB+MIe+2nd CBBUC6CB+MIe+2nd+hlclmb

in Tractable Learning of Large Bayes Net Structures from Sparse Data
by Anna Goldenberg 2004
"... In PAGE 5: ...eported in terms of the average BDeu score, i.e. the fi- nal BDeu score obtained by the network averaged over the number of records in the dataset. The number of edges in the resulting Bayes Nets is reported in Table5 . It is interest- ing to note that the BDeu scores correspondingto the Bayes Nets obtained by running CBBUC6CB as described in Table 1 are very close to the ones obtained by random hillclimbing, but have significantly lower number of edges.... ..."
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Table 4. Average BDeu scores. (D7 BP BG, mfss BP BG; BEBHBCBN BCBCBC random edges considered for hillclimbing) dataset rand hlclmb CBBUC6CB CBBUC6CB+MIe CBBUC6CB+MIe+2nd CBBUC6CB+MIe+2nd+hlclmb

in Tractable Learning of Large Bayes Net Structures from Sparse Data
by Anna Goldenberg 2004
"... In PAGE 5: ...We tested our algorithm in a variety of configurations on the datasets listed in Table 3. The results in Table4 are reported in terms of the average BDeu score, i.e.... ..."
Cited by 19

Table 5. Number of links in the resulting nets. (D7 BP BG, mfss BP BG; BDBCBCBN BCBCBC random edges considered for hillclimbing) dataset rand hlclmb CBBUC6CB CBBUC6CB+MIe CBBUC6CB+MIe+2nd CBBUC6CB+MIe+2nd+hlclmb

in Tractable Learning of Large Bayes Net Structures from Sparse Data
by Anna Goldenberg 2004
"... In PAGE 5: ...eported in terms of the average BDeu score, i.e. the fi- nal BDeu score obtained by the network averaged over the number of records in the dataset. The number of edges in the resulting Bayes Nets is reported in Table5 . It is interest- ing to note that the BDeu scores correspondingto the Bayes Nets obtained by running CBBUC6CB as described in Table 1 are very close to the ones obtained by random hillclimbing, but have significantly lower number of edges.... ..."
Cited by 19
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