17 research outputs found
Approximating the MaxCover Problem with Bounded Frequencies in FPT Time
We study approximation algorithms for several variants of the MaxCover
problem, with the focus on algorithms that run in FPT time. In the MaxCover
problem we are given a set N of elements, a family S of subsets of N, and an
integer K. The goal is to find up to K sets from S that jointly cover (i.e.,
include) as many elements as possible. This problem is well-known to be NP-hard
and, under standard complexity-theoretic assumptions, the best possible
polynomial-time approximation algorithm has approximation ratio (1 - 1/e). We
first consider a variant of MaxCover with bounded element frequencies, i.e., a
variant where there is a constant p such that each element belongs to at most p
sets in S. For this case we show that there is an FPT approximation scheme
(i.e., for each B there is a B-approximation algorithm running in FPT time) for
the problem of maximizing the number of covered elements, and a randomized FPT
approximation scheme for the problem of minimizing the number of elements left
uncovered (we take K to be the parameter). Then, for the case where there is a
constant p such that each element belongs to at least p sets from S, we show
that the standard greedy approximation algorithm achieves approximation ratio
exactly (1-e^{-max(pK/|S|, 1)}). We conclude by considering an unrestricted
variant of MaxCover, and show approximation algorithms that run in exponential
time and combine an exact algorithm with a greedy approximation. Some of our
results improve currently known results for MaxVertexCover
A Framework for Approval-based Budgeting Methods
We define and study a general framework for approval-based budgeting methods
and compare certain methods within this framework by their axiomatic and
computational properties. Furthermore, we visualize their behavior on certain
Euclidean distributions and analyze them experimentally
Parameterized Matroid-Constrained Maximum Coverage
In this paper, we introduce the concept of Density-Balanced Subset in a matroid, in which independent sets can be sampled so as to guarantee that (i) each element has the same probability to be sampled, and (ii) those events are negatively correlated. These Density-Balanced Subsets are subsets in the ground set of a matroid in which the traditional notion of uniform random sampling can be extended.
We then provide an application of this concept to the Matroid-Constrained Maximum Coverage problem. In this problem, given a matroid ? = (V, ?) of rank k on a ground set V and a coverage function f on V, the goal is to find an independent set S ? ? maximizing f(S). This problem is an important special case of the much-studied submodular function maximization problem subject to a matroid constraint; this is also a generalization of the maximum k-cover problem in a graph. In this paper, assuming that the coverage function has a bounded frequency ? (i.e., any element of the underlying universe of the coverage function appears in at most ? sets), we design a procedure, parameterized by some integer ?, to extract in polynomial time an approximate kernel of size ? ? k that is guaranteed to contain a 1 - (? - 1)/? approximation of the optimal solution. This procedure can then be used to get a Fixed-Parameter Tractable Approximation Scheme (FPT-AS) providing a 1 - ? approximation in time (?/?)^O(k) ? |V|^O(1). This generalizes and improves the results of [Manurangsi, 2019] and [Huang and Sellier, 2022], providing the first FPT-AS working on an arbitrary matroid. Moreover, as the kernel has a very simple characterization, it can be constructed in the streaming setting
Parameterized Exact and Approximation Algorithms for Maximum -Set Cover and Related Satisfiability Problems
Given a family of subsets over a set of elements~ and two
integers~ and~, Max k-Set Cover consists of finding a subfamily~ of cardinality at most~, covering at least~
elements of~. This problem is W[2]-hard when parameterized by~, and FPT
when parameterized by . We investigate the parameterized approximability of
the problem with respect to parameters~ and~. Then, we show that Max
Sat-k, a satisfiability problem generalizing Max k-Set Cover, is also FPT with
respect to parameter~.Comment: Accepted in RAIRO - Theoretical Informatics and Application
Multiwinner Elections with Diversity Constraints
We develop a model of multiwinner elections that combines performance-based
measures of the quality of the committee (such as, e.g., Borda scores of the
committee members) with diversity constraints. Specifically, we assume that the
candidates have certain attributes (such as being a male or a female, being
junior or senior, etc.) and the goal is to elect a committee that, on the one
hand, has as high a score regarding a given performance measure, but that, on
the other hand, meets certain requirements (e.g., of the form "at least
of the committee members are junior candidates and at least are
females"). We analyze the computational complexity of computing winning
committees in this model, obtaining polynomial-time algorithms (exact and
approximate) and NP-hardness results. We focus on several natural classes of
voting rules and diversity constraints.Comment: A short version of this paper appears in the proceedings of AAAI-1
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
Multiwinner Analogues of Plurality Rule: Axiomatic and Algorithmic Perspectives
We characterize the class of committee scoring rules that satisfy the
fixed-majority criterion. In some sense, the committee scoring rules in this
class are multiwinner analogues of the single-winner Plurality rule, which is
uniquely characterized as the only single-winner scoring rule that satisfies
the simple majority criterion. We define top--counting committee scoring
rules and show that the fixed majority consistent rules are a subclass of the
top--counting rules. We give necessary and sufficient conditions for a
top--counting rule to satisfy the fixed-majority criterion. We find that,
for most of the rules in our new class, the complexity of winner determination
is high (that is, the problem of computing the winners is NP-hard), but we also
show examples of rules with polynomial-time winner determination procedures.
For some of the computationally hard rules, we provide either exact FPT
algorithms or approximate polynomial-time algorithms