56,747 research outputs found
On the p-media polytope of special class of graphs
In this paper we consider a well known class of valid inequalities for the p-median and the uncapacitated facility location polytopes, the odd cycle inequalities. It is known that their separation problem is polynomially solvable. We give a new polynomial separation algorithm based on a reduction from the original graph. Then, we define a nontrivial class of graphs, where the odd cycle inequalities together with the linear relaxations of both the p-median and uncapacitated facility location problems, suffice to describe the associated polytope. To do this, we first give a complete description of the fractional extreme points of the linear relaxation for the p-median polytope in that class of graphs.Dans cet article nous étudions les inégalités de cycles impairs. Nous donnons un nouvel algorithme de séparation de ces inégalités, et nous montrons que lorsque nous ajoutons ces inégalités aux inégalités de la relaxation linéaire nous obtenons le polytope du p-médian dans la classe des graphes sans Y. Pour cela nous caractérisons d'abord les points extrêmesfractionnaires du système défini par la relaxation linéaire dans les graphes sans Y
Models for multi-depot routing problems
In this dissertation we study two problems. In the first part of the dissertation we study the multi-depot routing problem. In the multi-depot routing problem we are given a set of depots and a set of clients and the objective is to find a set of routes with minimum total cost, one for each depot, such that each route starts and ends at the same depot and all clients are visited in one and only one route. The requirement that routes must start and end at the same depot is modeled by so-called path elimination constraints. We present a formulation which includes a newly developed set of multi-cut path elimination constraints and a branch-and-cut algorithm based on the new formulation that it is able to solve both asymmetric and symmetric instances with up to 300 clients and 60 depots. Additionally, we present other approaches to model path elimination constraints, including a formulation which provides linear programming relaxation values which are close to the optimal value in the instances tested.
In the second part of the dissertation we study the Hamiltonian p-median problem. In the Hamiltonian p-median we are given a set of nodes and the objective is to find p circuits with minimum total cost such that each node is in one and only circuit. We propose a formulation based on the concept of acting depot which attributes the role of artificial depot to p of the nodes.
This formulation is a non-straightforward adaptation of the new model proposed for the multidepot routing problem and it is based on a novel idea in which the standard arc variables are split into three cases depending on whether none or exactly one of its endpoints is an acting depot. We present a branch-and-cut algorithm based on the new formulation which is able to solve asymmetric instances with up to 171 nodes and symmetric instances with up to 100 nodes.Programa de Bolsas de Doutoramento da Universidade de Lisbo
Relax, no need to round: integrality of clustering formulations
We study exact recovery conditions for convex relaxations of point cloud
clustering problems, focusing on two of the most common optimization problems
for unsupervised clustering: -means and -median clustering. Motivations
for focusing on convex relaxations are: (a) they come with a certificate of
optimality, and (b) they are generic tools which are relatively parameter-free,
not tailored to specific assumptions over the input. More precisely, we
consider the distributional setting where there are clusters in
and data from each cluster consists of points sampled from a
symmetric distribution within a ball of unit radius. We ask: what is the
minimal separation distance between cluster centers needed for convex
relaxations to exactly recover these clusters as the optimal integral
solution? For the -median linear programming relaxation we show a tight
bound: exact recovery is obtained given arbitrarily small pairwise separation
between the balls. In other words, the pairwise center
separation is . Under the same distributional model, the
-means LP relaxation fails to recover such clusters at separation as large
as . Yet, if we enforce PSD constraints on the -means LP, we get
exact cluster recovery at center separation .
In contrast, common heuristics such as Lloyd's algorithm (a.k.a. the -means
algorithm) can fail to recover clusters in this setting; even with arbitrarily
large cluster separation, k-means++ with overseeding by any constant factor
fails with high probability at exact cluster recovery. To complement the
theoretical analysis, we provide an experimental study of the recovery
guarantees for these various methods, and discuss several open problems which
these experiments suggest.Comment: 30 pages, ITCS 201
Algorithms for the continuous nonlinear resource allocation problem---new implementations and numerical studies
Patriksson (2008) provided a then up-to-date survey on the
continuous,separable, differentiable and convex resource allocation problem
with a single resource constraint. Since the publication of that paper the
interest in the problem has grown: several new applications have arisen where
the problem at hand constitutes a subproblem, and several new algorithms have
been developed for its efficient solution. This paper therefore serves three
purposes. First, it provides an up-to-date extension of the survey of the
literature of the field, complementing the survey in Patriksson (2008) with
more then 20 books and articles. Second, it contributes improvements of some of
these algorithms, in particular with an improvement of the pegging (that is,
variable fixing) process in the relaxation algorithm, and an improved means to
evaluate subsolutions. Third, it numerically evaluates several relaxation
(primal) and breakpoint (dual) algorithms, incorporating a variety of pegging
strategies, as well as a quasi-Newton method. Our conclusion is that our
modification of the relaxation algorithm performs the best. At least for
problem sizes up to 30 million variables the practical time complexity for the
breakpoint and relaxation algorithms is linear
Lagrangian Relaxation and Partial Cover
Lagrangian relaxation has been used extensively in the design of
approximation algorithms. This paper studies its strengths and limitations when
applied to Partial Cover.Comment: 20 pages, extended abstract appeared in STACS 200
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