3,311 research outputs found
Characterization of Cyclically Fully commutative elements in finite and affine Coxeter Groups
An element of a Coxeter group W is fully commutative if any two of its
reduced decompositions are related by a series of transpositions of adjacent
commuting generators. An element of a Coxeter group W is cyclically fully
commutative if any of its cyclic shifts remains fully commutative. These
elements were studied in Boothby et al.. In particular the authors enumerated
cyclically fully commutative elements in all Coxeter groups having a finite
number of them. In this work we characterize and enumerate cyclically fully
commutative elements according to their Coxeter length in all finite or affine
Coxeter groups by using a new operation on heaps, the cylindric transformation.
In finite types, this refines the work of Boothby et al., by adding a new
parameter. In affine type, all the results are new. In particular, we prove
that there is a finite number of cyclically fully commutative logarithmic
elements in all affine Coxeter groups. We study afterwards the cyclically fully
commutative involutions and prove that their number is finite in all Coxeter
groups.Comment: 23 pages, 16 figure
The Logarithmic Funnel Heap: A Statistically Self-Similar Priority Queue
The present work contains the design and analysis of a statistically
self-similar data structure using linear space and supporting the operations,
insert, search, remove, increase-key and decrease-key for a deterministic
priority queue in expected O(1) time. Extract-max runs in O(log N) time. The
depth of the data structure is at most log* N. On the highest level, each
element acts as the entrance of a discrete, log* N-level funnel with a
logarithmically decreasing stem diameter, where the stem diameter denotes a
metric for the expected number of items maintained on a given level.Comment: 14 pages, 4 figure
Summary-based inference of quantitative bounds of live heap objects
This article presents a symbolic static analysis for computing parametric upper bounds of the number of simultaneously live objects of sequential Java-like programs. Inferring the peak amount of irreclaimable objects is the cornerstone for analyzing potential heap-memory consumption of stand-alone applications or libraries. The analysis builds method-level summaries quantifying the peak number of live objects and the number of escaping objects. Summaries are built by resorting to summaries of their callees. The usability, scalability and precision of the technique is validated by successfully predicting the object heap usage of a medium-size, real-life application which is significantly larger than other previously reported case-studies.Fil: Braberman, Victor Adrian. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones CientÃficas y Técnicas; ArgentinaFil: Garbervetsky, Diego David. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones CientÃficas y Técnicas; ArgentinaFil: Hym, Samuel. Universite Lille 3; FranciaFil: Yovine, Sergio Fabian. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones CientÃficas y Técnicas; Argentin
QuickXsort: Efficient Sorting with n log n - 1.399n +o(n) Comparisons on Average
In this paper we generalize the idea of QuickHeapsort leading to the notion
of QuickXsort. Given some external sorting algorithm X, QuickXsort yields an
internal sorting algorithm if X satisfies certain natural conditions.
With QuickWeakHeapsort and QuickMergesort we present two examples for the
QuickXsort-construction. Both are efficient algorithms that incur approximately
n log n - 1.26n +o(n) comparisons on the average. A worst case of n log n +
O(n) comparisons can be achieved without significantly affecting the average
case.
Furthermore, we describe an implementation of MergeInsertion for small n.
Taking MergeInsertion as a base case for QuickMergesort, we establish a
worst-case efficient sorting algorithm calling for n log n - 1.3999n + o(n)
comparisons on average. QuickMergesort with constant size base cases shows the
best performance on practical inputs: when sorting integers it is slower by
only 15% to STL-Introsort
Q-systems, Heaps, Paths and Cluster Positivity
We consider the cluster algebra associated to the -system for as a
tool for relating -system solutions to all possible sets of initial data. We
show that the conserved quantities of the -system are partition functions
for hard particles on particular target graphs with weights, which are
determined by the choice of initial data. This allows us to interpret the
simplest solutions of the Q-system as generating functions for Viennot's heaps
on these target graphs, and equivalently as generating functions of weighted
paths on suitable dual target graphs. The generating functions take the form of
finite continued fractions. In this setting, the cluster mutations correspond
to local rearrangements of the fractions which leave their final value
unchanged. Finally, the general solutions of the -system are interpreted as
partition functions for strongly non-intersecting families of lattice paths on
target lattices. This expresses all cluster variables as manifestly positive
Laurent polynomials of any initial data, thus proving the cluster positivity
conjecture for the -system. We also give an alternative formulation in
terms of domino tilings of deformed Aztec diamonds with defects.Comment: 106 pages, 38 figure
QuickHeapsort: Modifications and improved analysis
We present a new analysis for QuickHeapsort splitting it into the analysis of
the partition-phases and the analysis of the heap-phases. This enables us to
consider samples of non-constant size for the pivot selection and leads to
better theoretical bounds for the algorithm. Furthermore we introduce some
modifications of QuickHeapsort, both in-place and using n extra bits. We show
that on every input the expected number of comparisons is n lg n - 0.03n + o(n)
(in-place) respectively n lg n -0.997 n+ o (n). Both estimates improve the
previously known best results. (It is conjectured in Wegener93 that the
in-place algorithm Bottom-Up-Heapsort uses at most n lg n + 0.4 n on average
and for Weak-Heapsort which uses n extra-bits the average number of comparisons
is at most n lg n -0.42n in EdelkampS02.) Moreover, our non-in-place variant
can even compete with index based Heapsort variants (e.g. Rank-Heapsort in
WangW07) and Relaxed-Weak-Heapsort (n lg n -0.9 n+ o (n) comparisons in the
worst case) for which no O(n)-bound on the number of extra bits is known
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