ON TWO APPROXIMATION ALGORITHMS FOR THE CLIQUE PROBLEM

1993 ◽  
Vol 04 (02) ◽  
pp. 117-133
Author(s):  
IAIN A. STEWART
Keyword(s):  
Approximation Algorithms ◽  
Polynomial Time ◽  
Optimization Problem ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Deterministic Algorithm ◽  
Time Approximation ◽  
Nondeterministic Algorithm ◽  
Polynomial Time Approximation ◽  
Clique Problem

We look at well-known polynomial-time approximation algorithms for the optimization problem MAX-CLIQUE (“find the size of the largest clique in a graph”) with regard to how easy it is to compute the actual cliques yielded by these approximation algorithms. We show that even for two “pretty useless” deterministic polynomial-time approximation algorithms, it is unlikely that the resulting clique can be computed efficiently in parallel. We also show that for each non-deterministic algorithm, it is unlikely that there is some deterministic polynomial-time algorithm that decides whether any given vertex appears in some clique yielded by that nondeterministic algorithm.

scholarly journals A POLYNOMIAL-TIME APPROXIMATION ALGORITHM FOR A GEOMETRIC DISPERSION PROBLEM

2009 ◽  
Vol 19 (03) ◽  
pp. 267-288 ◽  
Author(s):  
MARC BENKERT ◽  
JOACHIM GUDMUNDSSON ◽  
CHRISTIAN KNAUER ◽  
RENÉ VAN OOSTRUM ◽  
ALEXANDER WOLFF
Keyword(s):  
Approximation Algorithm ◽  
Polynomial Time ◽  
Packing Problem ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Time Approximation ◽  
Polynomial Time Approximation Algorithm ◽  
Dispersion Problem ◽  
Unit Disks ◽  
Polynomial Time Approximation

We consider the following packing problem. Let α be a fixed real in (0, 1]. We are given a bounding rectangle ρ and a set [Formula: see text] of n possibly intersecting unit disks whose centers lie in ρ. The task is to pack a set [Formula: see text] of m disjoint disks of radius α into ρ such that no disk in B intersects a disk in [Formula: see text], where m is the maximum number of unit disks that can be packed. In this paper we present a polynomial-time algorithm for α = 2/3. So far only the case of packing squares has been considered. For that case, Baur and Fekete have given a polynomial-time algorithm for α = 2/3 and have shown that the problem cannot be solved in polynomial time for any α > 13/14 unless [Formula: see text].

APPROXIMATION ALGORITHMS FOR SCHEDULING MALLEABLE TASKS UNDER PRECEDENCE CONSTRAINTS

2002 ◽  
Vol 13 (04) ◽  
pp. 613-627 ◽  
Author(s):  
RENAUD LEPÈRE ◽  
DENIS TRYSTRAM ◽  
GERHARD J. WOEGINGER
Keyword(s):  
Approximation Algorithms ◽  
Approximation Algorithm ◽  
Polynomial Time ◽  
Precedence Constraints ◽  
Performance Guarantee ◽  
Time Approximation ◽  
Polynomial Time Approximation Algorithm ◽  
Special Cases ◽  
Special Case ◽  
Polynomial Time Approximation

This work presents approximation algorithms for scheduling the tasks of a parallel application that are subject to precedence constraints. The considered tasks are malleable which means that they may be executed on a varying number of processors in parallel. The considered objective criterion is the makespan, i.e., the largest task completion time. We demonstrate a close relationship between this scheduling problem and one of its subproblems, the allotment problem. By exploiting this relationship, we design a polynomial time approximation algorithm with performance guarantee arbitrarily close to [Formula: see text] for the special case of series parallel precedence constraints and for the special case of precedence constraints of bounded width. These special cases cover the important situation of tree structured precedence constraints. For arbitrary precedence constraints, we give a polynomial time approximation algorithm with performance guarantee [Formula: see text].

Optimal Screening of Populations with Heterogeneous Risk Profiles Under the Availability of Multiple Tests

2021 ◽  
Author(s):  
Hrayer Aprahamian ◽  
Hadi El-Amine
Keyword(s):  
Objective Function ◽  
Structural Properties ◽  
Polynomial Time ◽  
Network Flow ◽  
Optimization Problem ◽  
Flow Problem ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Network Flow Problem ◽  
Multiple Tests

We study the design of large-scale group testing schemes under a heterogeneous population (i.e., subjects with potentially different risk) and with the availability of multiple tests. The objective is to classify the population as positive or negative for a given binary characteristic (e.g., the presence of an infectious disease) as efficiently and accurately as possible. Our approach examines components often neglected in the literature, such as the dependence of testing cost on the group size and the possibility of no testing, which are especially relevant within a heterogeneous setting. By developing key structural properties of the resulting optimization problem, we are able to reduce it to a network flow problem under a specific, yet not too restrictive, objective function. We then provide results that facilitate the construction of the resulting graph and finally provide a polynomial time algorithm. Our case study, on the screening of HIV in the United States, demonstrates the substantial benefits of the proposed approach over conventional screening methods. Summary of Contribution: This paper studies the problem of testing heterogeneous populations in groups in order to reduce costs and hence allow for the use of more efficient tests for high-risk groups. The resulting problem is a difficult combinatorial optimization problem that is NP-complete under a general objective. Using structural properties specific to our objective function, we show that the problem can be cast as a network flow problem and provide a polynomial time algorithm.

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