Greedy algorithms for on-line set-covering and related problems
نویسندگان
چکیده
We study the following on-line model for set-covering: elements of a ground set of size n arrive one-by-one and with any such element ci, arrives also the name of some set Si0 containing ci and covering the most of the uncovered ground set-elements (obviously, these elements have not been yet revealed). For this model we analyze a simple greedy algorithm consisting of taking Si0 into the cover, only if ci is not already covered. We prove that the competitive ratio of this algorithm is √ n and that it is asymptotically optimal for the model dealt, since no on-line algorithm can do better than √ n/2. We next show that this model can also be used for solving minimum dominating set with competitive ratio bounded above by the square root of the size of the input graph. We finally deal with the maximum budget saving problem. Here, an initial budget is allotted that is destined to cover the cost of an algorithm for solving set-covering. The objective is to maximize the savings on the initial budget. We show that when this budget is at least equal to √ n times the size of the optimal (off-line) solution of the instance under consideration, then the natural greedy off-line algorithm is asymptotically optimal.
منابع مشابه
Heuristic and exact algorithms for Generalized Bin Covering Problem
In this paper, we study the Generalized Bin Covering problem. For this problem an exact algorithm is introduced which can nd optimal solution for small scale instances. To nd a solution near optimal for large scale instances, a heuristic algorithm has been proposed. By computational experiments, the eciency of the heuristic algorithm is assessed.
متن کاملGreedy D{\ensuremath{\Delta}}-Approximation Algorithm for Covering with Arbitrary Constraints and Submodular Cost
This paper describes a greedy ∆-approximation algorithm for MONOTONE COVERING, a generalization of many fundamental NP-hard covering problems. The approximation ratio ∆ is the maximum number of variables on which any constraint depends. (For example, for vertex cover, ∆ is 2.) The algorithm unifies, generalizes, and improves many previous algorithms for fundamental covering problems such as ver...
متن کاملBiased and unbiased random-key genetic algorithms: An experimental analysis
We study the runtime performance of three types of randomkey genetic algorithms: the unbiased algorithm of Bean (1994); the biased algorithm of Gonçalves and Resende (2011); and a greedy version of Bean’s algorithm on 12 instances from four types of covering problems: general-cost set covering, Steiner triple covering, general-cost set k-covering, and unit-cost covering by pairs. The experiment...
متن کاملDiscrete Algorithms Seminar
We present a simple and unified approach for developing and analyzing approximation algorithms for covering problems. We illustrate this on approximation algorithms for the following problems: Vertex Cover, Set Cover, Feedback Vertex Set, Generalized Steiner Forest and related problems. The main idea can be phrased as follows: iteratively , pay two dollars (at most) to reduce the total optimum ...
متن کاملAn Experimental Comparison of Biased and Unbiased Random-key Genetic Algorithms
Random key genetic algorithms are heuristic methods for solving combinatorial optimization problems. They represent solutions as vectors of randomly generated real numbers, the so-called random keys. A deterministic algorithm, called a decoder, takes as input a vector of random keys and associates with it a feasible solution of the combinatorial optimization problem for which an objective value...
متن کامل