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Bribery in election (or computational social choice in general) is an important problem that has received a considerable amount of attention. In the classic bribery problem, the briber (or attacker) bribes some voters in attempting to make the bribers designated candidate win an election. In this paper, we introduce a novel variant of the bribery problem, Election with Bribed Voter Uncertainty or BVU for short, accommodating the uncertainty that the vote of a bribed voter may or may not be counted. This uncertainty occurs either because a bribed voter may not cast its vote in fear of being caught, or because a bribed voter is indeed caught and therefore its vote is discarded. As a first step towards ultimately understanding and addressing this important problem, we show that it does not admit any multiplicative $O(1)$-approximation algorithm modulo standard complexity assumptions. We further show that there is an approximation algorithm that returns a solution with an additive-$epsilon$ error in FPT time for any fixed $epsilon$.
The minimum linear ordering problem (MLOP) seeks to minimize an aggregated cost $f(cdot)$ due to an ordering $sigma$ of the items (say $[n]$), i.e., $min_{sigma} sum_{iin [n]} f(E_{i,sigma})$, where $E_{i,sigma}$ is the set of items that are mapped b
We study approximation algorithms for the problem of minimizing the makespan on a set of machines with uncertainty on the processing times of jobs. In the model we consider, which goes back to~cite{BertsimasS03}, once the schedule is defined an adver
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In mobile wireless sensor networks (MWSNs), each sensor has the ability not only to sense and transmit data but also to move to some specific location. Because the movement of sensors consumes much more power than that in sensing and communication, t
This paper describes a simple greedy D-approximation algorithm for any covering problem whose objective function is submodular and non-decreasing, and whose feasible region can be expressed as the intersection of arbitrary (closed upwards) covering c