3,298 research outputs found
Restricted Max-Min Allocation: Approximation and Integrality Gap
Asadpour, Feige, and Saberi proved that the integrality gap of the configuration LP for the restricted max-min allocation problem is at most 4. However, their proof does not give a polynomial-time approximation algorithm. A lot of efforts have been devoted to designing an efficient algorithm whose approximation ratio can match this upper bound for the integrality gap. In ICALP 2018, we present a (6 + delta)-approximation algorithm where delta can be any positive constant, and there is still a gap of roughly 2. In this paper, we narrow the gap significantly by proposing a (4+delta)-approximation algorithm where delta can be any positive constant. The approximation ratio is with respect to the optimal value of the configuration LP, and the running time is poly(m,n)* n^{poly(1/(delta))} where n is the number of players and m is the number of resources. We also improve the upper bound for the integrality gap of the configuration LP to 3 + 21/26 =~ 3.808
PTAS for Ordered Instances of Resource Allocation Problems
We consider the problem of fair allocation of indivisible goods where
we are given a set I of m indivisible resources (items) and a set P of n customers (players) competing for the resources. Each resource
j in I has a same value vj > 0 for a subset of customers interested in j and it has no value for other customers. The goal is to find a feasible allocation of the resources to the interested customers such that in the Max-Min scenario (also known as Santa Claus problem) the minimum utility (sum of the resources) received by each of the customers is as high as possible and in the Min-Max case (also known as R||C_max problem), the maximum utility is as low as possible.
In this paper we are interested in instances of the problem that admit a PTAS. These instances are not only of theoretical interest but also have practical applications. For the Max-Min allocation problem, we start with instances of the problem that can be viewed as a convex bipartite graph; there exists an ordering of the resources such that each customer is interested (has positive evaluation) in a set of consecutive resources and we demonstrate a PTAS. For the Min-Max allocation problem, we obtain a PTAS for instances in which there is an ordering of the customers (machines) and each resource (job) is adjacent to a consecutive set of customers (machines).
Next we show that our method for the Max-Min scenario, can be extended to a broader class of bipartite graphs where the resources can be viewed as a tree and each customer is interested in a sub-tree of a bounded number of leaves of this tree (e.g. a sub-path)
The Core of the Participatory Budgeting Problem
In participatory budgeting, communities collectively decide on the allocation
of public tax dollars for local public projects. In this work, we consider the
question of fairly aggregating the preferences of community members to
determine an allocation of funds to projects. This problem is different from
standard fair resource allocation because of public goods: The allocated goods
benefit all users simultaneously. Fairness is crucial in participatory decision
making, since generating equitable outcomes is an important goal of these
processes. We argue that the classic game theoretic notion of core captures
fairness in the setting. To compute the core, we first develop a novel
characterization of a public goods market equilibrium called the Lindahl
equilibrium, which is always a core solution. We then provide the first (to our
knowledge) polynomial time algorithm for computing such an equilibrium for a
broad set of utility functions; our algorithm also generalizes (in a
non-trivial way) the well-known concept of proportional fairness. We use our
theoretical insights to perform experiments on real participatory budgeting
voting data. We empirically show that the core can be efficiently computed for
utility functions that naturally model our practical setting, and examine the
relation of the core with the familiar welfare objective. Finally, we address
concerns of incentives and mechanism design by developing a randomized
approximately dominant-strategy truthful mechanism building on the exponential
mechanism from differential privacy
Coalitional Games in MISO Interference Channels: Epsilon-Core and Coalition Structure Stable Set
The multiple-input single-output interference channel is considered. Each
transmitter is assumed to know the channels between itself and all receivers
perfectly and the receivers are assumed to treat interference as additive
noise. In this setting, noncooperative transmission does not take into account
the interference generated at other receivers which generally leads to
inefficient performance of the links. To improve this situation, we study
cooperation between the links using coalitional games. The players (links) in a
coalition either perform zero forcing transmission or Wiener filter precoding
to each other. The -core is a solution concept for coalitional games
which takes into account the overhead required in coalition deviation. We
provide necessary and sufficient conditions for the strong and weak
-core of our coalitional game not to be empty with zero forcing
transmission. Since, the -core only considers the possibility of
joint cooperation of all links, we study coalitional games in partition form in
which several distinct coalitions can form. We propose a polynomial time
distributed coalition formation algorithm based on coalition merging and prove
that its solution lies in the coalition structure stable set of our coalition
formation game. Simulation results reveal the cooperation gains for different
coalition formation complexities and deviation overhead models.Comment: to appear in IEEE Transactions on Signal Processing, 14 pages, 14
figures, 3 table
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