Obtaining Initial Estimates of the Value Assignment and Calibration Curve Slope for the Stochastic

Approximation AlgorithmWe have designed the polygon

approximation algorithm and have analyzed the relation between the error and the number of polygon's edges.

In order to fulfil both the fault tolerance and energy efficiency requirements, we propose an efficient

approximation algorithm, Energy Efficient Maximum Disjoint Coverage (EMDC), with provable approximation bound.

By using an

approximation algorithm named Extended Hungarian is to minimize the number of steiner points on all the edges by means of extended cost in which the steiner points are calculated based on information about the nodes and the number of repeated values can be reduced.

For NP-hard problems, the research focuses on developing polynomial time

approximation algorithms. Given instance I of a minimization problem and

approximation algorithm A, let A(I) and OPT(1) denote the objective value of the solution obtained by algorithm A and the optimal solution value, respectively, when applied to I.

They give an

approximation algorithm for this problem when the sensor ranges are unit disks.

As discussed in the introduction, the best

approximation algorithm for the densest k-subgraph problem currently known has an approximation ratio of O([n.sup.1/4+[delta]]) for any fixed [delta] > 0 [4] and it is conjectured that the inapproximability of the problem is of a similar magnitude.

Then the flop count [F.sub.l] for the low-rank

approximation Algorithm 2 is

In [19], an 0(n log n) time 12-factor

approximation algorithm is proposed for the problem of covering a set of line segments with minimum number of sensors.

Zhu, "Optimized

approximation algorithm in neural networks without overfitting," IEEE Transactions on Neural Networks, vol.

In the last few years, there has been renewed interest in tackling this problem, this time from the perspective of

approximation algorithms.(2) In this paper, we carry this further by developing an

approximation algorithm based on the primal-dual schema.

IS is approximate equivalent to SP, in the sense that every

approximation algorithm solving the former also solves the latter within the same approximation ratio; this equivalence becomes very clear and intuitive by means of a graph (see Definition 3 at the beginning of Part II) defined for every SP-instance; proofs of this equivalence are found in Berge [1973] and Simon [1990].