### Table 1: Properties of Convex-Hull and Convex sets.

1994

"... In PAGE 2: ... 100]), meaning that the conventional Convex-Hull is indeed a particular case of the generalized Convex-Hull. Table1 shows that some of the basic properties of the Convex-Hull and of Convex sets are naturally extended to the B-Convex-Hull operation and to B-Convex sets.... ..."

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### Table 1 Convex

"... In PAGE 4: ... The next step of the analysis, however, provided a more promising result. As shown in Table1 , the number of people visible from each convex space was consistently correlated not only with the visual range of the space but also with its integration into the setting as a whole. That more people are visible from spaces which have a stronger visual range is hardly surprising.... In PAGE 5: ... However, these correlations were neither very strong or consistent. Table1 . Correlation between the Number of People Visible from Each Convex Space with Convex Configuration Variables.... ..."

### Table 1: Time to compute the Convex Hull for n

1995

"... In PAGE 4: ... The actual imple- mentation allows us to examine some factors which cannot be determined solely by theoretical analysis. The results are shown in Table1 for sets from 50 to 20000 points in size. These points were obtained by randomly generating their x and y coordinates in the range 0 to 5000.... In PAGE 4: ... These points were obtained by randomly generating their x and y coordinates in the range 0 to 5000. The result in Table1 is obtained for each input size by taking the mean of the time to compute the convex hull points from over 60 di#0Berent random generated data sets. Since there is no built in clock for the WAVETRACER, the running time for each random generated data set is obtained by tim- ing 100 executions of the algorithm and then dividing by 100.... ..."

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### Table 3: A set of 12 points whose convex hull is not castable.

"... In PAGE 31: ...Table 3: A set of 12 points whose convex hull is not castable. According to an exhaustive checking of all possible planes through three vertices, the convex hull of the set of points given in Table3 is not castable. The points were generated at random, near the surface of a sphere.... ..."

### Table 6: Commands for dealing with non-convex piecewise linear sets.

"... In PAGE 25: ... For convenience, we build a separate toolkit in order to deal with non-convex Pwl sets. Table6 lists the commands for operations on Pwl sets.... ..."

### Table 2: Duality for closed conic convex programs

"... In PAGE 23: ...d #03 = inf 8 #3E #3C #3E : s 5 #0C #0C #0C #0C #0C #0C #0C 2 6 4 0 1 0 1 s 2 s 5 = p 2 0 s 5 = p 2 0 3 7 5 #17 0 9 #3E = #3E ; = 1: Finally, the possibility of the entries in Table2 where weak infeasibility is not involved, can be demonstrated by a 2-dimensional linear programming problem: Example 5 Let n =2,c2#3C 2 ,K=K #03 =#3C 2 + and A = f#28x 1 ;x 2 #29jx 1 =0g; A ? =f#28s 1 ;s 2 #29js 2 =0g: We see that #28P#29 is strongly feasible if c 1 #3E 0,weakly feasible if c 1 =0and strongly infeasible if c 1 #3C 0. Similarly, #28D#29 is strongly feasible if c 2 #3E 0,weakly feasible if c 2 =0and strongly infeasible if c 2 #3C 0.... In PAGE 27: ... #0F The regularizedprogram CP#28b; c; A; K 0 #29 is dual strongly infeasible if and only if F D = ;. Combining Theorem 8 with Table2 , we see that the regularized conic convex program is in perfect duality: Corollary 7 Assume the same setting as in Theorem 8. Then there holds #0F If d #03 = 1, then the regularized primal CP#28b; c; A; K 0 #29 is either infeasible or unbounded.... ..."

### Table 7.4: Total number of variables, constraints and enforced planar edge pairs of the Vienna MIP for six different planarity settings.

2005

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### Table 7.8: Total number of variables, constraints and enforced planar edge pairs of the Sydney MIP for six different planarity settings.

2005

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### Table 7.10: Total number of variables, constraints and enforced planar edge pairs of the London MIP for six different planarity settings.

2005

Cited by 1

### Table 3: Experiments with a simple planar graph 4 Drawing a Simple Planar Graph The graph used in the following experiments is an extension of the graph presented by Nishizeki and Chiba21 as an example of planar graph that haven apos;t convex drawing. So, the algorithm presented in21 can apos;t draw it.

"... In PAGE 7: ... Next, we summarize the experiments we did for illustrating the robustness of the proposed approach. The experiments, functions involved and coe cients are in Table3 , and the GA parameters are in Table 4. Unif corresponds to the uniform crossover originally proposed by Syswerda.... ..."