By Bednorz W.

ISBN-10: 9537619273

ISBN-13: 9789537619275

Bednorz W. Advances in grasping algorithms (In-Teh, 2008)(ISBN 9537619273)(596s)_CsAl_

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**Example text**

In addition, let nv = {Qv ∩ C} be the number of uncovered elements in the set Qv, for every v ∈ V , at the beginning of each iteration. The algorithm works as follows. It initializes C ← Z. Then, in each iteration, it selects the set Qv with the A Greedy Scheme for Designing Delay Monitoring Systems of IP Networks minimum 25 ratio and removes all its elements from the set C. This step is done until C becomes empty. A formal description of the algorithm is presented in Figure 3. Theorem 3 The greedy algorithm computes a ln(│V│)-approximation for the LM and WLM problems.

Subsets, as Proof: Let J be a bad SC instance with m elements and constructed in [26] for proving the ln(m) lower bound for the SC problem. Let (J) be the graph calculated by . The lower bound for the PM problem, PM(│V│), satisfies, The number of nodes in the graph (J) is for a large m we assume that │V│ m and thus, PM(│V│) ≥ ln(│V│). 2 A greedy algorithm for station selection Similar to the WLM problem, an efficient solution to a WPM instance is obtained by mapping it to an instance of the CS problem and using the greedy heuristic given in Figure 3 to solve this problem.

At each walk-step, at least one unsatisfied clause becomes satisfied. The other criterion uses a greedy search to choose a random variable from the set PossFlips. Each variable in this set, when flipped, can achieve the largest decrease (or the least increase) in the total number of unsatisfied clauses. The walk-step strategy may lead to an increase in the total number of unsatisfied clauses even if improving flips would have been possible. In consequence, the chances of escaping from local minima of the objective function are better compared with the basic GSAT [11].

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