Sentence examples for approximation bound from inspiring English sources

Our greedy selection algorithm provides an approximation bound of 1−1/√e, where e is the base of the natural logarithm.

In our theoretical analysis, we show that our Greedy Forwarding achieves in the worst case a 3.291 path stretch approximation bound with respect to the shortest path, without assuming presence of symmetrical links or unit disk graphs.

They are the first known theoretical approximation bound results for the problems of minimizing the total costs (including both the edge and the bend costs) while spanning all given subsets of vertices.

Our analysis of the greedy algorithm shows that, when applied to covering a bipartite graph using copies of Kq,q bicliques, it returns a feasible solution whose cost is at most (Hq2−Hq+1 OPT+1 where OPT denotes the optimal cost, thus improving the approximation bound for unweighted q2-set cover by a factor of almost 2.

We prove a lower approximation bound of 8√5−15≈2.88854, improving the previous bound of 10√5−21≈1.36067 by Dinur and Safra [The importance of being biased, Proceedings of the 34th Annual ACM Symposium on Theory of Computing (STOC), May 2002, pp. 33 42, also ECCC Report TR01-104, 2001].

Moreover, we have that this approximation bound is tight.

The following simple greedy algorithm [a special case of the algorithm proposed in (Bar-Noy et al., 2001)] can be shown to be a 2-approximation: In practice we expect the greedy algorithm to return much better solutions than the provable 2-approximation bound.

3.3, we use the discretized version of that estimate (which gives the l2 approximation error bound) in order to find as small as possible approximation generator with sufficiently small approximation error for every patch.

The merits of the proposed control scheme are not only that the conservative estimation of NFN approximation error bound is avoided but also that a suitable-sized neural structure is found to sufficiently approximate the system uncertainties.

Such bounds have utility both for robust control law design and for self-organizing approximators that could adjust the number of basis elements N by adding additional approximation resources in the regions where the approximation error bound is large.

We have also shown the stability of the ENO-wavelet transforms and obtained a rigorous approximation error bound which shows that the error in the ENO-wavelet approximation depends only on the size of the derivative of the function away from the discontinuities.