 heuristic. The estimation of Pr(X 0 ≤ X 0) is computed for N = 000, 000, 000, 000, 000, 000, 000, 000, 000, 0000, 0000, 0000, 0000, and 0000 independent runs of each algorithm. Table 0.0 shows the results, which are also displayed in Figure 0.00. Once again, the estimation of Pr(X 0 ≤ X 0) stabilizes as the sample size N increases.  Fig. 0.00  Convergence of the estimation of Pr(X 0 ≤ X 0) with the sample size for the 00-node instance of the 0-path network design problem.  Table 0.0  Convergence of the estimation of Pr(X 0 ≤ X 0) with the sample size for the 00-node instance of the 0-path network design problem.  N | L(ɛ) | Pr(X 0 ≤ X 0) | R(ɛ) | Δ(ɛ) | ɛ  ---|---|---|---|---|---  000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0. 000000 × 00−0  000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0. 000000 × 00−0  000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0. 000000 × 00−0  000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0. 000000 × 00−0  000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0. 000000 × 00−0  000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0. 000000 × 00−0  000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0. 000000 × 00−0  000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0. 000000 × 00−0  000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0. 000000 × 00−0  0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0. 000000 × 00−0  0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0. 000000 × 00−0  0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0. 000000 × 00−0  0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0. 000000 × 00−0  0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0. 000000 × 00−0  