Efficient algorithms for optimal matching problems under preferences

Kwanashie, Augustine (2015) Efficient algorithms for optimal matching problems under preferences. PhD thesis, University of Glasgow.

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Printed Thesis Information: https://eleanor.lib.gla.ac.uk/record=b3127646


In this thesis we consider efficient algorithms for matching problems involving preferences,
i.e., problems where agents may be required to list other agents that they find
acceptable in order of preference. In particular we mainly study the Stable Marriage
problem (SM), the Hospitals / Residents problem (HR) and the Student / Project Allocation
problem (SPA), and some of their variants. In some of these problems the aim
is to find a stable matching which is one that admits no blocking pair. A blocking pair
with respect to a matching is a pair of agents that prefer to be matched to each other
than their assigned partners in the matching if any.
We present an Integer Programming (IP) model for the Hospitals / Residents problem
with Ties (HRT) and use it to find a maximum cardinality stable matching. We also
present results from an empirical evaluation of our model which show it to be scalable
with respect to real-world HRT instance sizes.

Motivated by the observation that not all blocking pairs that exist in theory will lead
to a matching being undermined in practice, we investigate a relaxed stability criterion
called social stability where only pairs of agents with a social relationship have the
ability to undermine a matching. This stability concept is studied in instances of
the Stable Marriage problem with Incomplete lists (smi) and in instances of hr. We
show that, in the smi and hr contexts, socially stable matchings can be of varying
sizes and the problem of finding a maximum socially stable matching (max smiss and
max hrss respectively) is NP-hard though approximable within 3/2. Furthermore we
give polynomial time algorithms for three special cases of the problem arising from
restrictions on the social network graph and the lengths of agents’ preference lists.
We also consider other optimality criteria with respect to social stability and establish
inapproximability bounds for the problems of finding an egalitarian, minimum regret
and sex equal socially stable matching in the sm context.

We extend our study of social stability by considering other variants and restrictions
of max smiss and max hrss. We present NP-hardness results for max smiss even
under certain restrictions on the degree and structure of the social network graph as
well as the presence of master lists. Other NP-hardness results presented relate to the
problem of determining whether a given man-woman pair belongs to a socially stable
matching and the problem of determining whether a given man (or woman) is part of
at least one socially stable matching. We also consider the Stable Roommates problem
with Incomplete lists under Social Stability (a non-bipartite generalisation of smi under
social stability). We observe that the problem of finding a maximum socially stable
matching in this context is also NP-hard. We present efficient algorithms for three
special cases of the problem arising from restrictions on the social network graph and
the lengths of agents’ preference lists. These are the cases where (i) there exists a
constant number of acquainted pairs (ii) or a constant number of unacquainted pairs
or (iii) each preference list is of length at most 2.

We also present algorithmic results for finding matchings in the spa context that are
optimal with respect to profile, which is the vector whose ith component is the number
of students assigned to their ith-choice project. We present an efficient algorithm for
finding a greedy maximum matching in the spa context — this is a maximum matching
whose profile is lexicographically maximum. We then show how to adapt this algorithm
to find a generous maximum matching — this is a matching whose reverse profile is
lexicographically minimum. We demonstrate how this approach can allow additional
constraints, such as lecturer lower quotas, to be handled flexibly. We also present
results of empirical evaluations carried out on both real world and randomly generated
datasets. These results demonstrate the scalability of our algorithms as well as some
interesting properties of these profile-based optimality criteria.

Practical applications of spa motivate the investigation of certain special cases of the
problem. For instance, it is often desired that the workload on lecturers is evenly distributed
(i.e. load balanced). We enforce this by either adding lower quota constraints
on the lecturers (which leads to the potential for infeasible problem instances) or adding
a load balancing optimisation criterion. We present efficient algorithms in both cases.
Another consideration is the fact that certain projects may require a minimum number
of students to become viable. This can be handled by enforcing lower quota constraints
on the projects (which also leads to the possibility of infeasible problem instances). A
technique of handling this infeasibility is the idea of closing projects that do not meet
their lower quotas (i.e. leaving such project completely unassigned). We show that the
problem of finding a maximum matching subject to project lower quotas where projects
can be closed is NP-hard even under severe restrictions on preference lists lengths and
project upper and lower quotas. To offset this hardness, we present polynomial time
heuristics that find large feasible matchings in practice. We also present ip models
for the spa variants discussed and show results obtained from an empirical evaluation
carried out on both real and randomly generated datasets. These results show that
our algorithms and heuristics are scalable and provide good matchings with respect to
profile-based optimality

Item Type: Thesis (PhD)
Qualification Level: Doctoral
Keywords: stable matching, student project allocation, hospitals residents problem, algorithms, matching
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Q Science > QA Mathematics > QA76 Computer software
Colleges/Schools: College of Science and Engineering > School of Computing Science
Supervisor's Name: Manlove, Dr. David
Date of Award: 2015
Depositing User: Mr Augustine Kwanashie
Unique ID: glathesis:2015-6706
Copyright: Copyright of this thesis is held by the author.
Date Deposited: 25 Sep 2015 07:25
Last Modified: 05 Oct 2015 11:45
URI: https://theses.gla.ac.uk/id/eprint/6706

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