### Table 8: Statistics for 2D divide-and-conquer Delaunay triangulation of several point sets. Timings are

1997

"... In PAGE 50: ... (I have also tried perfect lattices with 53-bit integer coordinates, but ORIENT3D and INSPHERE never pass stage B; the perturbed lattices are preferred here because they occasionally force the predicates into stage C or D.) The results for 2D, which appear in Table8 , indicate that the four-stage predicates add about 8% to the total running time for randomly distributed input points, mainly because of the error bound tests. For the more difficult point sets, the penalty may be as great as 30%.... ..."

Cited by 86

### Table 3: Statistics for 2D divide-and-conquer Delaunay triangulation of several point sets.

1996

"... In PAGE 10: ...lattices with 53-bit integer coordinates, but ORIENT3D and INSPHERE would never pass stage B; the perturbed lattices occasionally force the predicates into stage C or D.) The results for 2D, outlined in Table3 , indicate that the four-stage predicates add about 8% to the total running time for randomly distributed input points, mainly because of the errorboundtests. Forthe more difficult point sets, the penalty may be as great as 30%.... ..."

Cited by 47

### Table 4 Search time by plane-sweep and divide-and-conquer algorithms

### Table II presents a summary of the different biclustering algorithms in accordance with the different dimensions of analysis considered. The second column classifies the algorithms according to the type of biclusters they aim at finding (see Section III). Column three lists the biclustering approaches according to the bicluster structure they can produce. The notation used is the one in Fig. IV in Section IV. The last two columns summarize Section V by classifying the different algorithms according to the way they discover the biclusters and the approach they use to achieve their goal. The notation used is the following: iterative row and column clustering combination (Clust-Comb), divide-and-conquer (Div-Conq), greedy iterative search (Greedy), exhaustive bicluster enumeration (Exh-Enum) and distribution parameter identification (Dist-Based).

### Table 1: Experimental Results

1996

"... In PAGE 11: ... 6.0 Experimental Results Table1 shows throughput improvements achieved using the best previous approach and using the divide- and-conquer approach. Results for both no unfolding and non-restricted amounts of unfolding are shown.... ..."

Cited by 3

### Table 1: Results on 26 small circuits with 22 or less PIs. pi gate exhaustive gate replace divide-and-conquer circuit

2005

"... In PAGE 3: ... Our results are compared with traditional input vector control methods in terms of leakage saving, run time, area and delay penalty. We conducted ex- periments on 69 benchmarks including 26 small circuits with 22 or fewer primary inputs ( Table1 ) and 43 large circuits (Figure 4). For each small circuit, we find its optimal MLV by exhaustive search.... In PAGE 3: ... To have a fair comparison with [1], we also collect the average leakage of 1,000 random input vectors for each large circuit. Table1 reports the results for the 26 small circuits. Column 4... ..."

Cited by 3

### Table 4.3. For k divisions by the same divisor, the constant c such that multi- plication in time O(nc) makes Newton and divide-and-conquer division equally fast asymptotically.

### Table 7.14: Comparison of the flat SVM, Divide-and-Conquer SVM, and hierarchical SVM on some sections of the multiclass WIPO-alpha corpus.

2008

### Table 1: Workspace Classes

"... In PAGE 3: ...The classes defined in Table1 suggest both visual (colour and texture) and 3D geometrical features. Our vehicle is equipped with a 3D laser scanner, which supplies direct measurements of geometry.... In PAGE 3: ... Thus, a 3D point cloud is assembled which represents the original scene subject to the colour image. The structural and ground classes in Table1 can be approximated geometrically with a planar model. Therefore, the 3D laser data associated with an image were segmented into planes following a divide-and-conquer approach outlined in [9]: a given point cloud is discretised into cubic cells and planes are fitted locally using RANSAC [10].... ..."

### Table 7.19: Experiments of Divide-and-Conquer SVM using random hierarchies on Newsgroup data, with t = 20. The last five rows employ the same 5 random hierar- chies as in Table 7.18.

2008