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Cited by 68 publications
(119 citation statements)
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“…A decision epoch is a period of time during which only the alarms which have arrived are considered for assignment. It is shown in literature [17] that the optimal solution to online TA assigns the tasks in a greedy fashion. Also [17] show that the greedy online TA solution is within a bounded limit of an optimal solution obtained by offline TA.…”
Section: Task Allocation: Offline Vs Onlinementioning
confidence: 99%
See 1 more Smart Citation
“…A decision epoch is a period of time during which only the alarms which have arrived are considered for assignment. It is shown in literature [17] that the optimal solution to online TA assigns the tasks in a greedy fashion. Also [17] show that the greedy online TA solution is within a bounded limit of an optimal solution obtained by offline TA.…”
Section: Task Allocation: Offline Vs Onlinementioning
confidence: 99%
“…It is shown in literature [17] that the optimal solution to online TA assigns the tasks in a greedy fashion. Also [17] show that the greedy online TA solution is within a bounded limit of an optimal solution obtained by offline TA. In general, increasing the decision epoch to infinity turns the online TA into offline TA problem.…”
Section: Task Allocation: Offline Vs Onlinementioning
confidence: 99%
“…Let us finally return to the weighted bipartite matching problem, whose on-line complexity has been analyzed by Kalyanasundaram and Pruhs (1993). The model assumes an underlying complete bipartite graph Kn, ~ = (V1 w V2, E) on 2n vertices.…”
Section: On-line Algorithmsmentioning
confidence: 99%
“…A surprising result of Bartal et al (1992) shows that the greedy algorithm for makespan minimization for job shop problems can measurably be beaten when one passes from pure list scheduling to balanced list scheduling. The situation is even worse for the greedy algorithm for the linear assignment problem, where the performance is an exponential factor off the online optimum (Kalyanasundaram and Pruhs (1993)). Boruvka (1926) showed that the greedy algorithm is successful provided J is a matroid, i.e., satisfies in addition to (2.0) and (2.1) It turns out that many structures in algorithmic combinatorics satisfy properties (2.0) and (2.2) but not necessarily (2.1) (e.g., the bisimplicial elimination schemes below).…”
Section: Introductionmentioning
confidence: 99%
“…The problem has found applications in other related problems (e.g., [ANR02], [AR05], [AC06]). The problem has also been studied for general weighted graphs in [KP93] where a 1/3-competitive deterministic algorithm is given. In [Sit96], a formula for the cardinality of the maximum matching in complete multipartite graph is given.…”
Section: Introductionmentioning
confidence: 99%