Spelling suggestions: "subject:"1inear discrepancy"" "subject:"cinear discrepancy""
1 |
A study of discrepancy results in partially ordered setsHoward, David M. 20 May 2010 (has links)
In 2001, Fishburn, Tanenbaum, and Trenk published a pair of papers that introduced the notions of linear and weak discrepancy of a partially ordered set or poset. Linear discrepancy for a poset is the least k such that for any ordering of the points in the poset there is a pair of incomparable points at least distance k away in the ordering. Weak discrepancy is similar to linear discrepancy except that the distance is observed over weak labelings (i.e. two points can have the same label if they are incomparable, but order is still preserved). My thesis gives a variety of results pertaining to these properties and other forms of discrepancy in posets. The first chapter of my thesis partially answers a question of Fishburn, Tanenbaum, and Trenk that was to characterize those posets with linear discrepancy two. It makes the characterization for those posets with width two and references the paper where the full characterization is given. The second chapter introduces the notion of t-discrepancy which is similar to weak discrepancy except only the weak labelings with
at most t copies of any label are considered. This chapter shows that determining a poset's t-discrepancy is NP-Complete. It also gives the t-discrepancy for the disjoint sum of chains and provides a polynomial time algorithm for determining t-discrepancy of semiorders. The third chapter presents another notion of discrepancy namely total discrepancy which minimizes the average distance between incomparable elements. This chapter proves that finding this value can be done in polynomial time unlike linear discrepancy and t-discrepancy. The final chapter answers another question of Fishburn, Tanenbaum, and Trenk that asked to characterize those posets that have equal linear and weak discrepancies. Though determining the answer of whether the weak discrepancy and linear discrepancy of a poset are equal is an NP-Complete problem, the set of minimal posets that have this property are given. At the end of the thesis I discuss two other open problems not mentioned in the previous chapters that relate to linear discrepancy. The first asks if there is a link between a poset's dimension and its linear discrepancy. The second refers to approximating linear discrepancy and possible ways to do it.
|
2 |
Some results on linear discrepancy for partially ordered setsKeller, Mitchel Todd 24 November 2009 (has links)
Tanenbaum, Trenk, and Fishburn introduced the concept of linear
discrepancy in 2001, proposing it as a way to measure a partially
ordered set's distance from being a linear order. In addition to
proving a number of results about linear discrepancy, they posed
eight challenges and questions for future work. This dissertation
completely resolves one of those challenges and makes contributions
on two others. This dissertation has three principal components:
3-discrepancy irreducible posets of width 3, degree bounds, and
online algorithms for linear discrepancy. The first principal
component of this dissertation provides a forbidden subposet
characterization of the posets with linear discrepancy equal to 2
by completing the determination of the posets that are
3-irreducible with respect to linear discrepancy. The second
principal component concerns degree bounds for linear discrepancy
and weak discrepancy, a parameter similar to linear
discrepancy. Specifically, if every point of a poset is incomparable
to at most D other points of the poset, we prove three
bounds: the linear discrepancy of an interval order is at most
D, with equality if and only if it contains an antichain of
size D; the linear discrepancy of a disconnected poset is
at most the greatest integer less than or equal to (3D-1)/2; and the weak discrepancy of a
poset is at most D. The third principal component of this
dissertation incorporates another large area of research, that of
online algorithms. We show that no online algorithm for linear
discrepancy can be better than 3-competitive, even for the class
of interval orders. We also give a 2-competitive online algorithm
for linear discrepancy on semiorders and show that this algorithm is
optimal.
|
Page generated in 0.0775 seconds