393 research outputs found
Bi-Criteria and Approximation Algorithms for Restricted Matchings
In this work we study approximation algorithms for the \textit{Bounded Color
Matching} problem (a.k.a. Restricted Matching problem) which is defined as
follows: given a graph in which each edge has a color and a profit
, we want to compute a maximum (cardinality or profit)
matching in which no more than edges of color are
present. This kind of problems, beside the theoretical interest on its own
right, emerges in multi-fiber optical networking systems, where we interpret
each unique wavelength that can travel through the fiber as a color class and
we would like to establish communication between pairs of systems. We study
approximation and bi-criteria algorithms for this problem which are based on
linear programming techniques and, in particular, on polyhedral
characterizations of the natural linear formulation of the problem. In our
setting, we allow violations of the bounds and we model our problem as a
bi-criteria problem: we have two objectives to optimize namely (a) to maximize
the profit (maximum matching) while (b) minimizing the violation of the color
bounds. We prove how we can "beat" the integrality gap of the natural linear
programming formulation of the problem by allowing only a slight violation of
the color bounds. In particular, our main result is \textit{constant}
approximation bounds for both criteria of the corresponding bi-criteria
optimization problem
Approximation Algorithms for Union and Intersection Covering Problems
In a classical covering problem, we are given a set of requests that we need
to satisfy (fully or partially), by buying a subset of items at minimum cost.
For example, in the k-MST problem we want to find the cheapest tree spanning at
least k nodes of an edge-weighted graph. Here nodes and edges represent
requests and items, respectively.
In this paper, we initiate the study of a new family of multi-layer covering
problems. Each such problem consists of a collection of h distinct instances of
a standard covering problem (layers), with the constraint that all layers share
the same set of requests. We identify two main subfamilies of these problems: -
in a union multi-layer problem, a request is satisfied if it is satisfied in at
least one layer; - in an intersection multi-layer problem, a request is
satisfied if it is satisfied in all layers. To see some natural applications,
consider both generalizations of k-MST. Union k-MST can model a problem where
we are asked to connect a set of users to at least one of two communication
networks, e.g., a wireless and a wired network. On the other hand, intersection
k-MST can formalize the problem of connecting a subset of users to both
electricity and water.
We present a number of hardness and approximation results for union and
intersection versions of several standard optimization problems: MST, Steiner
tree, set cover, facility location, TSP, and their partial covering variants
A Constant Approximation for Colorful k-Center
In this paper, we consider the colorful k-center problem, which is a generalization of the well-known k-center problem. Here, we are given red and blue points in a metric space, and a coverage requirement for each color. The goal is to find the smallest radius rho, such that with k balls of radius rho, the desired number of points of each color can be covered. We obtain a constant approximation for this problem in the Euclidean plane. We obtain this result by combining a "pseudo-approximation" algorithm that works in any metric space, and an approximation algorithm that works for a special class of instances in the plane. The latter algorithm uses a novel connection to a certain matching problem in graphs
Budgeted Matroid Maximization: a Parameterized Viewpoint
We study budgeted variants of well known maximization problems with multiple
matroid constraints. Given an -matchoid \cm on a ground set , a
profit function , a cost function , and a budget , the
goal is to find in the -matchoid a feasible set of maximum profit
subject to the budget constraint, i.e., . The {\em budgeted
-matchoid} (BM) problem includes as special cases budgeted
-dimensional matching and budgeted -matroid intersection. A strong
motivation for studying BM from parameterized viewpoint comes from the
APX-hardness of unbudgeted -dimensional matching (i.e., )
already for . Nevertheless, while there are known FPT algorithms for
the unbudgeted variants of the above problems, the {\em budgeted} variants are
studied here for the first time through the lens of parameterized complexity.
We show that BM parametrized by solution size is -hard, already with a
degenerate single matroid constraint. Thus, an exact parameterized algorithm is
unlikely to exist, motivating the study of {\em FPT-approximation schemes}
(FPAS). Our main result is an FPAS for BM (implying an FPAS for
-dimensional matching and budgeted -matroid intersection), relying
on the notion of representative set a small cardinality subset of elements
which preserves the optimum up to a small factor. We also give a lower bound on
the minimum possible size of a representative set which can be computed in
polynomial time
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