251 research outputs found
Generalized Laminar Matroids
Nested matroids were introduced by Crapo in 1965 and have appeared frequently
in the literature since then. A flat of a matroid is Hamiltonian if it has
a spanning circuit. A matroid is nested if and only if its Hamiltonian
flats form a chain under inclusion; is laminar if and only if, for every
-element independent set , the Hamiltonian flats of containing
form a chain under inclusion. We generalize these notions to define the classes
of -closure-laminar and -laminar matroids. This paper focuses on
structural properties of these classes noting that, while the second class is
always minor-closed, the first is if and only if . The main results
are excluded-minor characterizations for the classes of 2-laminar and
2-closure-laminar matroids.Comment: 12 page
Laminar Matroids
A laminar family is a collection of subsets of a set such
that, for any two intersecting sets, one is contained in the other. For a
capacity function on , let be \{I:|I\cap A|
\leq c(A)\text{ for all A\in\mathscr{A}}\}. Then is the
collection of independent sets of a (laminar) matroid on . We present a
method of compacting laminar presentations, characterize the class of laminar
matroids by their excluded minors, present a way to construct all laminar
matroids using basic operations, and compare the class of laminar matroids to
other well-known classes of matroids.Comment: 17 page
Matroidal Degree-Bounded Minimum Spanning Trees
We consider the minimum spanning tree (MST) problem under the restriction
that for every vertex v, the edges of the tree that are adjacent to v satisfy a
given family of constraints. A famous example thereof is the classical
degree-constrained MST problem, where for every vertex v, a simple upper bound
on the degree is imposed. Iterative rounding/relaxation algorithms became the
tool of choice for degree-bounded network design problems. A cornerstone for
this development was the work of Singh and Lau, who showed for the
degree-bounded MST problem how to find a spanning tree violating each degree
bound by at most one unit and with cost at most the cost of an optimal solution
that respects the degree bounds.
However, current iterative rounding approaches face several limits when
dealing with more general degree constraints. In particular, when several
constraints are imposed on the edges adjacent to a vertex v, as for example
when a partition of the edges adjacent to v is given and only a fixed number of
elements can be chosen out of each set of the partition, current approaches
might violate each of the constraints by a constant, instead of violating all
constraints together by at most a constant number of edges. Furthermore, it is
also not clear how previous iterative rounding approaches can be used for
degree constraints where some edges are in a super-constant number of
constraints.
We extend iterative rounding/relaxation approaches both on a conceptual level
as well as aspects involving their analysis to address these limitations. This
leads to an efficient algorithm for the degree-constrained MST problem where
for every vertex v, the edges adjacent to v have to be independent in a given
matroid. The algorithm returns a spanning tree T of cost at most OPT, such that
for every vertex v, it suffices to remove at most 8 edges from T to satisfy the
matroidal degree constraint at v
On Generalizations of Network Design Problems with Degree Bounds
Iterative rounding and relaxation have arguably become the method of choice
in dealing with unconstrained and constrained network design problems. In this
paper we extend the scope of the iterative relaxation method in two directions:
(1) by handling more complex degree constraints in the minimum spanning tree
problem (namely, laminar crossing spanning tree), and (2) by incorporating
`degree bounds' in other combinatorial optimization problems such as matroid
intersection and lattice polyhedra. We give new or improved approximation
algorithms, hardness results, and integrality gaps for these problems.Comment: v2, 24 pages, 4 figure
The Submodular Secretary Problem Goes Linear
During the last decade, the matroid secretary problem (MSP) became one of the
most prominent classes of online selection problems. Partially linked to its
numerous applications in mechanism design, substantial interest arose also in
the study of nonlinear versions of MSP, with a focus on the submodular matroid
secretary problem (SMSP). So far, O(1)-competitive algorithms have been
obtained for SMSP over some basic matroid classes. This created some hope that,
analogously to the matroid secretary conjecture, one may even obtain
O(1)-competitive algorithms for SMSP over any matroid. However, up to now, most
questions related to SMSP remained open, including whether SMSP may be
substantially more difficult than MSP; and more generally, to what extend MSP
and SMSP are related.
Our goal is to address these points by presenting general black-box
reductions from SMSP to MSP. In particular, we show that any O(1)-competitive
algorithm for MSP, even restricted to a particular matroid class, can be
transformed in a black-box way to an O(1)-competitive algorithm for SMSP over
the same matroid class. This implies that the matroid secretary conjecture is
equivalent to the same conjecture for SMSP. Hence, in this sense SMSP is not
harder than MSP. Also, to find O(1)-competitive algorithms for SMSP over a
particular matroid class, it suffices to consider MSP over the same matroid
class. Using our reductions we obtain many first and improved O(1)-competitive
algorithms for SMSP over various matroid classes by leveraging known algorithms
for MSP. Moreover, our reductions imply an O(loglog(rank))-competitive
algorithm for SMSP, thus, matching the currently best asymptotic algorithm for
MSP, and substantially improving on the previously best
O(log(rank))-competitive algorithm for SMSP
Prophet Inequalities with Limited Information
In the classical prophet inequality, a gambler observes a sequence of
stochastic rewards and must decide, for each reward ,
whether to keep it and stop the game or to forfeit the reward forever and
reveal the next value . The gambler's goal is to obtain a constant
fraction of the expected reward that the optimal offline algorithm would get.
Recently, prophet inequalities have been generalized to settings where the
gambler can choose items, and, more generally, where he can choose any
independent set in a matroid. However, all the existing algorithms require the
gambler to know the distribution from which the rewards are
drawn.
The assumption that the gambler knows the distribution from which
are drawn is very strong. Instead, we work with the much simpler
assumption that the gambler only knows a few samples from this distribution. We
construct the first single-sample prophet inequalities for many settings of
interest, whose guarantees all match the best possible asymptotically,
\emph{even with full knowledge of the distribution}. Specifically, we provide a
novel single-sample algorithm when the gambler can choose any elements
whose analysis is based on random walks with limited correlation. In addition,
we provide a black-box method for converting specific types of solutions to the
related \emph{secretary problem} to single-sample prophet inequalities, and
apply it to several existing algorithms. Finally, we provide a constant-sample
prophet inequality for constant-degree bipartite matchings.
We apply these results to design the first posted-price and multi-dimensional
auction mechanisms with limited information in settings with asymmetric
bidders
On the Construction of Substitutes
Gross substitutability is a central concept in Economics and is connected to
important notions in Discrete Convex Analysis, Number Theory and the analysis
of Greedy algorithms in Computer Science. Many different characterizations are
known for this class, but providing a constructive description remains a major
open problem. The construction problem asks how to construct all gross
substitutes from a class of simpler functions using a set of operations. Since
gross substitutes are a natural generalization of matroids to real-valued
functions, matroid rank functions form a desirable such class of simpler
functions.
Shioura proved that a rich class of gross substitutes can be expressed as
sums of matroid rank functions, but it is open whether all gross substitutes
can be constructed this way. Our main result is a negative answer showing that
some gross substitutes cannot be expressed as positive linear combinations of
matroid rank functions. En route, we provide necessary and sufficient
conditions for the sum to preserve substitutability, uncover a new operation
preserving substitutability and fully describe all substitutes with at most 4
items
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