### 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 3 Convex Axial

"... In PAGE 6: ...486 Note. For explanation of variables see notes to Table 1 Tracking frequencies, by contrast, were most clearly and consistently correlated with connectivity, as indicated by Table3 . These correlations were not only significant statistically but also quite strong.... In PAGE 6: ... Table3 . Correlation between Tracking Frequency and Configuration Variables.... ..."

### 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.... ..."

Cited by 1

### Table 1: Values of the convex aggregation.

"... In PAGE 7: ... None of the images was misclassified. The values for the aggregation are in Table1 . The corresponding boosting map is shown in Fig 5.... ..."

### Table 4 Results on solving the four 730-hour non-convex models and their convex Model Non-convex

2005

### Table 11: Impact of non-convexity

2007

"... In PAGE 26: ...Table 11: Impact of non-convexity These cases are analyzed in Table11 , where, in percentages, \robust nominal quot; is the nominal return attained by the optimal solution to the robust optimization problem and \robust worst case quot; is the worst-case return it attains under the uncertainty model; \robust positions quot; is the number of positions taken by the robust portfolio. From an aggregate perspective all six cases are equivalent: the adversary can, in each case, decrease returns by a total \mass quot; of 100.... In PAGE 26: ... From an aggregate perspective all six cases are equivalent: the adversary can, in each case, decrease returns by a total \mass quot; of 100. Yet, as we can see from Table11 , the six cases are structurally quite di erent. It appears, therefore, that a smooth convex model used to replace our histogram structure would likely produce very di erent results in at least some of the six cases.... ..."

### 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.... ..."

Cited by 7

### 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.... ..."