When a worse approximation factor gives better performance: a 3-approximation algorithm for the vertex k-center problem

2017 ◽  
Vol 23 (5) ◽  
pp. 349-366 ◽  
Author(s):  
Jesus Garcia-Diaz ◽  
Jairo Sanchez-Hernandez ◽  
Ricardo Menchaca-Mendez ◽  
Rolando Menchaca-Mendez
2020 ◽  
Vol 31 (02) ◽  
pp. 275-291 ◽  
Author(s):  
Manjanna Basappa ◽  
Ramesh K. Jallu ◽  
Gautam K. Das

In this paper, we consider a restricted covering problem, in which a convex polygon [Formula: see text] with [Formula: see text] vertices and an integer [Formula: see text] are given, the objective is to cover the entire region of [Formula: see text] using [Formula: see text] congruent disks of minimum radius [Formula: see text], centered on the boundary of [Formula: see text]. For [Formula: see text] and any [Formula: see text], we propose an [Formula: see text]-factor approximation algorithm for this problem, which runs in [Formula: see text] time. The best known approximation factor of the algorithm for the problem in the literature is 1.8841 [H. Du and Y. Xu: An approximation algorithm for [Formula: see text]-center problem on a convex polygon, J. Comb. Optim. 27(3) (2014) 504–518].


2019 ◽  
Vol 30 (03) ◽  
pp. 425-448 ◽  
Author(s):  
Barun Gorain ◽  
Partha Sarathi Mandal

Time-varying coverage, namely sweep coverage is a recent development in the area of wireless sensor networks, where a few mobile sensors sweep or monitor a comparatively large number of locations periodically. In this article, we study barrier sweep coverage with mobile sensors where the barrier is considered as a finite length continuous curve on a plane. The coverage at every point on the curve is time-variant. We propose an optimal solution for sweep coverage of a finite length continuous curve. Usually, energy source of a mobile sensor is a battery with limited power, so energy restricted sweep coverage is a challenging problem for long running applications. We propose an energy-restricted sweep coverage problem where every mobile sensor must visit an energy source frequently to recharge or replace its battery. We propose a [Formula: see text]-approximation algorithm for this problem. The proposed algorithm for multiple curves achieves the best possible approximation factor 2 for a special case. We propose a 5-approximation algorithm for the general problem. As an application of the barrier sweep coverage problem for a set of line segments, we formulate a data gathering problem. In this problem a set of mobile sensors is arbitrarily monitoring the line segments one for each. A set of data mules periodically collects the monitoring data from the set of mobile sensors. We prove that finding the minimum number of data mules to collect data periodically from every mobile sensor is NP-hard and propose a 3-approximation algorithm to solve it.


1998 ◽  
Vol 27 (2) ◽  
pp. 259-268 ◽  
Author(s):  
Rina Panigrahy ◽  
Sundar Vishwanathan

2010 ◽  
Vol 43 (6-7) ◽  
pp. 572-586 ◽  
Author(s):  
Sorelle A. Friedler ◽  
David M. Mount

2015 ◽  
Vol 31 (4) ◽  
pp. 1417-1429 ◽  
Author(s):  
Dongyue Liang ◽  
Liquan Mei ◽  
James Willson ◽  
Wei Wang

2004 ◽  
Vol 32 (5) ◽  
pp. 491-495 ◽  
Author(s):  
Jochen Könemann ◽  
Yanjun Li ◽  
Ojas Parekh ◽  
Amitabh Sinha

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Klairton L. Brito ◽  
Andre R. Oliveira ◽  
Alexsandro O. Alexandrino ◽  
Ulisses Dias ◽  
Zanoni Dias

Abstract Background In the comparative genomics field, one of the goals is to estimate a sequence of genetic changes capable of transforming a genome into another. Genome rearrangement events are mutations that can alter the genetic content or the arrangement of elements from the genome. Reversal and transposition are two of the most studied genome rearrangement events. A reversal inverts a segment of a genome while a transposition swaps two consecutive segments. Initial studies in the area considered only the order of the genes. Recent works have incorporated other genetic information in the model. In particular, the information regarding the size of intergenic regions, which are structures between each pair of genes and in the extremities of a linear genome. Results and conclusions In this work, we investigate the sorting by intergenic reversals and transpositions problem on genomes sharing the same set of genes, considering the cases where the orientation of genes is known and unknown. Besides, we explored a variant of the problem, which generalizes the transposition event. As a result, we present an approximation algorithm that guarantees an approximation factor of 4 for both cases considering the reversal and transposition (classic definition) events, an improvement from the 4.5-approximation previously known for the scenario where the orientation of the genes is unknown. We also present a 3-approximation algorithm by incorporating the generalized transposition event, and we propose a greedy strategy to improve the performance of the algorithms. We performed practical tests adopting simulated data which indicated that the algorithms, in both cases, tend to perform better when compared with the best-known algorithms for the problem. Lastly, we conducted experiments using real genomes to demonstrate the applicability of the algorithms.


2018 ◽  
Vol 10 (06) ◽  
pp. 1850072
Author(s):  
Manjanna Basappa ◽  
Gautam K. Das

In this paper, we consider the discrete unit square cover (DUSC) problem as follows: given a set [Formula: see text] of [Formula: see text] points and a set [Formula: see text] of [Formula: see text] axis-aligned unit squares in [Formula: see text], the objective is (i) to check whether the union of the squares in [Formula: see text] covers all the points in [Formula: see text], and (ii) if the answer is yes, then select a minimum cardinality subset [Formula: see text] such that each point in [Formula: see text] is covered by at least one square in [Formula: see text]. For the DUSC problem:(i)we propose a [Formula: see text]-approximation algorithm, where [Formula: see text] is an integer parameter that defines a trade-off between the running time and the approximation factor of the algorithm. The running time of our proposed algorithm is [Formula: see text]. Our solution of the DUSC problem is based on a simple [Formula: see text]-approximation algorithm for the subproblem strip square cover (SSC) problem, where all the points in [Formula: see text] are lying within a horizontal strip of unit height.(ii)we also propose a 2-approximation algorithm, which runs in [Formula: see text] time. The 2-approximation algorithm is based on an algorithm for the subproblem SSC problem. The algorithm for the subproblem is developed using plane sweep and graph search traversal techniques. We also extend this algorithm to get 2-approximation result for the weighted DUSC problem where the squares are assigned weights, and the aim is to choose a subset [Formula: see text] such that each point in [Formula: see text] is covered by at least one square in [Formula: see text] and the sum of the weights of squares in [Formula: see text] is minimized.


2021 ◽  
Author(s):  
Mahsa Derakhshan ◽  
Negin Golrezaei ◽  
Renato Paes Leme

We study the problem of computing data-driven personalized reserve prices in eager second price auctions without having any assumption on valuation distributions. Here, the input is a data set that contains the submitted bids of n buyers in a set of auctions, and the problem is to return personalized reserve prices r that maximize the revenue earned on these auctions by running eager second price auctions with reserve r. For this problem, which is known to be NP complete, we present a novel linear program (LP) formulation and a rounding procedure, which achieves a 0.684 approximation. This improves over the [Formula: see text]-approximation algorithm from Roughgarden and Wang. We show that our analysis is tight for this rounding procedure. We also bound the integrality gap of the LP, which shows that it is impossible to design an algorithm that yields an approximation factor larger than 0.828 with respect to this LP.


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