106,462 research outputs found
A study of discrepancy results in partially ordered sets
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.Ph.D.Committee Chair: Trotter, William T.; Committee Member: Dieci, Luca; Committee Member: Duke, Richard; Committee Member: Randall, Dana; Committee Member: Tetali, Prasa
Decomposition Based Search - A theoretical and experimental evaluation
In this paper we present and evaluate a search strategy called Decomposition
Based Search (DBS) which is based on two steps: subproblem generation and
subproblem solution. The generation of subproblems is done through value
ranking and domain splitting. Subdomains are explored so as to generate,
according to the heuristic chosen, promising subproblems first.
We show that two well known search strategies, Limited Discrepancy Search
(LDS) and Iterative Broadening (IB), can be seen as special cases of DBS. First
we present a tuning of DBS that visits the same search nodes as IB, but avoids
restarts. Then we compare both theoretically and computationally DBS and LDS
using the same heuristic. We prove that DBS has a higher probability of being
successful than LDS on a comparable number of nodes, under realistic
assumptions. Experiments on a constraint satisfaction problem and an
optimization problem show that DBS is indeed very effective if compared to LDS.Comment: 16 pages, 8 figures. LIA Technical Report LIA00203, University of
Bologna, 200
Vote Miscounts or Exit Poll Error?
Electronic vote counting equipment makes it easy for a few persons to electronically manipulate vote counts and virtually impossible to independently audit vote count accuracy. Without routine independent audits of hand-countable voter verified paper ballots, insiders have freedom to manipulate vote counts with negligible possibility of detection.This new exit poll discrepancy function allows us, for the first time, to know what patterns of exit poll discrepancy result from various combinations ofvote miscounts,partisan exit poll completion rate differences, andrandom sampling error (by simulation).These patterns can be compared with actual discrepancy patterns produced in elections to determine what causes are consistent with actual election discrepancy.This paper also outlines statistical methods to determine the number of precincts with significant discrepancy, and how to test precinct-level exit poll and official election results data for consistency with partisan exit poll response rate explanations. Using these methods, mathematicians or statisticians can determine if vote miscounts are indicated prior to when candidates concede future elections
Structure-Aware Sampling: Flexible and Accurate Summarization
In processing large quantities of data, a fundamental problem is to obtain a
summary which supports approximate query answering. Random sampling yields
flexible summaries which naturally support subset-sum queries with unbiased
estimators and well-understood confidence bounds.
Classic sample-based summaries, however, are designed for arbitrary subset
queries and are oblivious to the structure in the set of keys. The particular
structure, such as hierarchy, order, or product space (multi-dimensional),
makes range queries much more relevant for most analysis of the data.
Dedicated summarization algorithms for range-sum queries have also been
extensively studied. They can outperform existing sampling schemes in terms of
accuracy on range queries per summary size. Their accuracy, however, rapidly
degrades when, as is often the case, the query spans multiple ranges. They are
also less flexible - being targeted for range sum queries alone - and are often
quite costly to build and use.
In this paper we propose and evaluate variance optimal sampling schemes that
are structure-aware. These summaries improve over the accuracy of existing
structure-oblivious sampling schemes on range queries while retaining the
benefits of sample-based summaries: flexible summaries, with high accuracy on
both range queries and arbitrary subset queries
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