4,206 research outputs found
Complexity of Token Swapping and its Variants
In the Token Swapping problem we are given a graph with a token placed on
each vertex. Each token has exactly one destination vertex, and we try to move
all the tokens to their destinations, using the minimum number of swaps, i.e.,
operations of exchanging the tokens on two adjacent vertices. As the main
result of this paper, we show that Token Swapping is -hard parameterized
by the length of a shortest sequence of swaps. In fact, we prove that, for
any computable function , it cannot be solved in time where is the number of vertices of the input graph, unless the ETH
fails. This lower bound almost matches the trivial -time algorithm.
We also consider two generalizations of the Token Swapping, namely Colored
Token Swapping (where the tokens have different colors and tokens of the same
color are indistinguishable), and Subset Token Swapping (where each token has a
set of possible destinations). To complement the hardness result, we prove that
even the most general variant, Subset Token Swapping, is FPT in nowhere-dense
graph classes.
Finally, we consider the complexities of all three problems in very
restricted classes of graphs: graphs of bounded treewidth and diameter, stars,
cliques, and paths, trying to identify the borderlines between polynomial and
NP-hard cases.Comment: 23 pages, 7 Figure
Axiomatic Characterization of Data-Driven Influence Measures for Classification
We study the following problem: given a labeled dataset and a specific
datapoint x, how did the i-th feature influence the classification for x? We
identify a family of numerical influence measures - functions that, given a
datapoint x, assign a numeric value phi_i(x) to every feature i, corresponding
to how altering i's value would influence the outcome for x. This family, which
we term monotone influence measures (MIM), is uniquely derived from a set of
desirable properties, or axioms. The MIM family constitutes a provably sound
methodology for measuring feature influence in classification domains; the
values generated by MIM are based on the dataset alone, and do not make any
queries to the classifier. While this requirement naturally limits the scope of
our framework, we demonstrate its effectiveness on data
Parameterized Approximation Schemes using Graph Widths
Combining the techniques of approximation algorithms and parameterized
complexity has long been considered a promising research area, but relatively
few results are currently known. In this paper we study the parameterized
approximability of a number of problems which are known to be hard to solve
exactly when parameterized by treewidth or clique-width. Our main contribution
is to present a natural randomized rounding technique that extends well-known
ideas and can be used for both of these widths. Applying this very generic
technique we obtain approximation schemes for a number of problems, evading
both polynomial-time inapproximability and parameterized intractability bounds
Stable Roommate Problem with Diversity Preferences
In the multidimensional stable roommate problem, agents have to be allocated
to rooms and have preferences over sets of potential roommates. We study the
complexity of finding good allocations of agents to rooms under the assumption
that agents have diversity preferences [Bredereck et al., 2019]: each agent
belongs to one of the two types (e.g., juniors and seniors, artists and
engineers), and agents' preferences over rooms depend solely on the fraction of
agents of their own type among their potential roommates. We consider various
solution concepts for this setting, such as core and exchange stability, Pareto
optimality and envy-freeness. On the negative side, we prove that envy-free,
core stable or (strongly) exchange stable outcomes may fail to exist and that
the associated decision problems are NP-complete. On the positive side, we show
that these problems are in FPT with respect to the room size, which is not the
case for the general stable roommate problem. Moreover, for the classic setting
with rooms of size two, we present a linear-time algorithm that computes an
outcome that is core and exchange stable as well as Pareto optimal. Many of our
results for the stable roommate problem extend to the stable marriage problem.Comment: accepted to IJCAI'2
Parameterized Complexity of Maximum Happy Set and Densest k-Subgraph
We present fixed-parameter tractable (FPT) algorithms for two problems,
Maximum Happy Set (MaxHS) and Maximum Edge Happy Set (MaxEHS)--also known as
Densest k-Subgraph. Given a graph and an integer , MaxHS asks for a set
of vertices such that the number of with
respect to is maximized, where a vertex is happy if and all its
neighbors are in . We show that MaxHS can be solved in time
and , where and denote the
and the of , respectively.
This resolves the open questions posed in literature. The MaxEHS problem is an
edge-variant of MaxHS, where we maximize the number of ,
the edges whose endpoints are in . In this paper we show that MaxEHS can be
solved in time and
, where
and denote the
and the of , respectively, and is
some computable function. This result implies that MaxEHS is also
fixed-parameter tractable by
Constrained Signaling in Auction Design
We consider the problem of an auctioneer who faces the task of selling a good
(drawn from a known distribution) to a set of buyers, when the auctioneer does
not have the capacity to describe to the buyers the exact identity of the good
that he is selling. Instead, he must come up with a constrained signalling
scheme: a (non injective) mapping from goods to signals, that satisfies the
constraints of his setting. For example, the auctioneer may be able to
communicate only a bounded length message for each good, or he might be legally
constrained in how he can advertise the item being sold. Each candidate
signaling scheme induces an incomplete-information game among the buyers, and
the goal of the auctioneer is to choose the signaling scheme and accompanying
auction format that optimizes welfare. In this paper, we use techniques from
submodular function maximization and no-regret learning to give algorithms for
computing constrained signaling schemes for a variety of constrained signaling
problems
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