28,338 research outputs found
Bounds on Integrals with Respect to Multivariate Copulas
Finding upper and lower bounds to integrals with respect to copulas is a
quite prominent problem in applied probability. In their 2014 paper, Hofer and
Iaco showed how particular two dimensional copulas are related to optimal
solutions of the two dimensional assignment problem. Using this, they managed
to approximate integrals with respect to two dimensional copulas. In this
paper, we will further illuminate this connection, extend it to d-dimensional
copulas and therefore generalize the method from Hofer and Iaco to arbitrary
dimensions. We also provide convergence statements. As an example, we consider
three dimensional dependence measures
Near-Optimal and Robust Mechanism Design for Covering Problems with Correlated Players
We consider the problem of designing incentive-compatible, ex-post
individually rational (IR) mechanisms for covering problems in the Bayesian
setting, where players' types are drawn from an underlying distribution and may
be correlated, and the goal is to minimize the expected total payment made by
the mechanism. We formulate a notion of incentive compatibility (IC) that we
call {\em support-based IC} that is substantially more robust than Bayesian IC,
and develop black-box reductions from support-based-IC mechanism design to
algorithm design. For single-dimensional settings, this black-box reduction
applies even when we only have an LP-relative {\em approximation algorithm} for
the algorithmic problem. Thus, we obtain near-optimal mechanisms for various
covering settings including single-dimensional covering problems, multi-item
procurement auctions, and multidimensional facility location.Comment: Major changes compared to the previous version. Please consult this
versio
Bounding Stochastic Dependence, Complete Mixability of Matrices, and Multidimensional Bottleneck Assignment Problems
We call a matrix completely mixable if the entries in its columns can be
permuted so that all row sums are equal. If it is not completely mixable, we
want to determine the smallest maximal and largest minimal row sum attainable.
These values provide a discrete approximation of of minimum variance problems
for discrete distributions, a problem motivated by the question how to estimate
the -quantile of an aggregate random variable with unknown dependence
structure given the marginals of the constituent random variables. We relate
this problem to the multidimensional bottleneck assignment problem and show
that there exists a polynomial -approximation algorithm if the matrix has
only columns. In general, deciding complete mixability is
-complete. In particular the swapping algorithm of Puccetti et
al. is not an exact method unless . For a
fixed number of columns it remains -complete, but there exists a
PTAS. The problem can be solved in pseudopolynomial time for a fixed number of
rows, and even in polynomial time if all columns furthermore contain entries
from the same multiset
Planar 3-dimensional assignment problems with Monge-like cost arrays
Given an cost array we consider the problem -P3AP
which consists in finding pairwise disjoint permutations
of such that
is minimized. For the case
the planar 3-dimensional assignment problem P3AP results.
Our main result concerns the -P3AP on cost arrays that are layered
Monge arrays. In a layered Monge array all matrices that result
from fixing the third index are Monge matrices. We prove that the -P3AP
and the P3AP remain NP-hard for layered Monge arrays. Furthermore, we show that
in the layered Monge case there always exists an optimal solution of the
-3PAP which can be represented as matrix with bandwidth . This
structural result allows us to provide a dynamic programming algorithm that
solves the -P3AP in polynomial time on layered Monge arrays when is
fixed.Comment: 16 pages, appendix will follow in v
The Quadratic Cycle Cover Problem: special cases and efficient bounds
The quadratic cycle cover problem is the problem of finding a set of
node-disjoint cycles visiting all the nodes such that the total sum of
interaction costs between consecutive arcs is minimized. In this paper we study
the linearization problem for the quadratic cycle cover problem and related
lower bounds.
In particular, we derive various sufficient conditions for the quadratic cost
matrix to be linearizable, and use these conditions to compute bounds. We also
show how to use a sufficient condition for linearizability within an iterative
bounding procedure. In each step, our algorithm computes the best equivalent
representation of the quadratic cost matrix and its optimal linearizable matrix
with respect to the given sufficient condition for linearizability. Further, we
show that the classical Gilmore-Lawler type bound belongs to the family of
linearization based bounds, and therefore apply the above mentioned iterative
reformulation technique. We also prove that the linearization vectors resulting
from this iterative approach satisfy the constant value property.
The best among here introduced bounds outperform existing lower bounds when
taking both quality and efficiency into account
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