81,117 research outputs found
Fat Polygonal Partitions with Applications to Visualization and Embeddings
Let be a rooted and weighted tree, where the weight of any node
is equal to the sum of the weights of its children. The popular Treemap
algorithm visualizes such a tree as a hierarchical partition of a square into
rectangles, where the area of the rectangle corresponding to any node in
is equal to the weight of that node. The aspect ratio of the
rectangles in such a rectangular partition necessarily depends on the weights
and can become arbitrarily high.
We introduce a new hierarchical partition scheme, called a polygonal
partition, which uses convex polygons rather than just rectangles. We present
two methods for constructing polygonal partitions, both having guarantees on
the worst-case aspect ratio of the constructed polygons; in particular, both
methods guarantee a bound on the aspect ratio that is independent of the
weights of the nodes.
We also consider rectangular partitions with slack, where the areas of the
rectangles may differ slightly from the weights of the corresponding nodes. We
show that this makes it possible to obtain partitions with constant aspect
ratio. This result generalizes to hyper-rectangular partitions in
. We use these partitions with slack for embedding ultrametrics
into -dimensional Euclidean space: we give a -approximation algorithm for embedding -point ultrametrics
into with minimum distortion, where denotes the spread
of the metric, i.e., the ratio between the largest and the smallest distance
between two points. The previously best-known approximation ratio for this
problem was polynomial in . This is the first algorithm for embedding a
non-trivial family of weighted-graph metrics into a space of constant dimension
that achieves polylogarithmic approximation ratio.Comment: 26 page
Sarissa
W ostatnim okresie daje się zauważyć wzrost zainteresowania wojskowością czasów
Filipa II Macedońskiego i Aleksandra III. Jedno z czołowych miejsc wśród studiowanych
zagadnień zajmuje problem sarissy.
W pracy podjęto próbę wskazania elementów sarissy w materiale archeologicznym.
W pierwszej kolejności analizowano kwestię grotu oraz drzewca sarissy. Dane archeologiczne
zestawiono z tekstami Ksenofonta, Diodora, Plutarcha, Grattiusa oraz Teofrasta. Zwrócono
uwagę na zawarte w nich niejasności, a także odniesiono się do szeregu dotychczasowych
błędnych interpretacji.
W efekcie sądzić można, że macedońska sarissa to broń o takich samych cechach jak
pika znana z czasów późniejszych. Dlatego też w artykule odniesiono się wielokrotnie do broni
nowożytnej. W związku z powyższym wykazano na przykład, iż nic mają podstawy sądy
o dużych grotach broni Macedończyków.Zadanie pt. „Digitalizacja i udostępnienie w Cyfrowym Repozytorium Uniwersytetu Łódzkiego kolekcji czasopism naukowych wydawanych przez Uniwersytet Łódzki” nr 885/P-DUN/2014 dofinansowane zostało ze środków MNiSW w ramach działalności upowszechniającej naukę
Dynamic Ordered Sets with Exponential Search Trees
We introduce exponential search trees as a novel technique for converting
static polynomial space search structures for ordered sets into fully-dynamic
linear space data structures.
This leads to an optimal bound of O(sqrt(log n/loglog n)) for searching and
updating a dynamic set of n integer keys in linear space. Here searching an
integer y means finding the maximum key in the set which is smaller than or
equal to y. This problem is equivalent to the standard text book problem of
maintaining an ordered set (see, e.g., Cormen, Leiserson, Rivest, and Stein:
Introduction to Algorithms, 2nd ed., MIT Press, 2001).
The best previous deterministic linear space bound was O(log n/loglog n) due
Fredman and Willard from STOC 1990. No better deterministic search bound was
known using polynomial space.
We also get the following worst-case linear space trade-offs between the
number n, the word length w, and the maximal key U < 2^w: O(min{loglog n+log
n/log w, (loglog n)(loglog U)/(logloglog U)}). These trade-offs are, however,
not likely to be optimal.
Our results are generalized to finger searching and string searching,
providing optimal results for both in terms of n.Comment: Revision corrects some typoes and state things better for
applications in subsequent paper
On the performance of a cavity method based algorithm for the Prize-Collecting Steiner Tree Problem on graphs
We study the behavior of an algorithm derived from the cavity method for the
Prize-Collecting Steiner Tree (PCST) problem on graphs. The algorithm is based
on the zero temperature limit of the cavity equations and as such is formally
simple (a fixed point equation resolved by iteration) and distributed
(parallelizable). We provide a detailed comparison with state-of-the-art
algorithms on a wide range of existing benchmarks networks and random graphs.
Specifically, we consider an enhanced derivative of the Goemans-Williamson
heuristics and the DHEA solver, a Branch and Cut Linear/Integer Programming
based approach. The comparison shows that the cavity algorithm outperforms the
two algorithms in most large instances both in running time and quality of the
solution. Finally we prove a few optimality properties of the solutions
provided by our algorithm, including optimality under the two post-processing
procedures defined in the Goemans-Williamson derivative and global optimality
in some limit cases
Longest Common Separable Pattern between Permutations
In this article, we study the problem of finding the longest common separable
pattern between several permutations. We give a polynomial-time algorithm when
the number of input permutations is fixed and show that the problem is NP-hard
for an arbitrary number of input permutations even if these permutations are
separable. On the other hand, we show that the NP-hard problem of finding the
longest common pattern between two permutations cannot be approximated better
than within a ratio of (where is the size of an optimal
solution) when taking common patterns belonging to pattern-avoiding classes of
permutations.Comment: 15 page
Isotropic Dynamic Hierarchical Clustering
We face a need of discovering a pattern in locations of a great number of
points in a high-dimensional space. Goal is to group the close points together.
We are interested in a hierarchical structure, like a B-tree. B-Trees are
hierarchical, balanced, and they can be constructed dynamically. B-Tree
approach allows to determine the structure without any supervised learning or a
priori knowlwdge. The space is Euclidean and isotropic. Unfortunately, there
are no B-Tree implementations processing indices in a symmetrical and
isotropical way. Some implementations are based on constructing compound
asymmetrical indices from point coordinates; and the others split the nodes
along the coordinate hyper-planes. We need to process tens of millions of
points in a thousand-dimensional space. The application has to be scalable.
Ideally, a cluster should be an ellipsoid, but it would require to store O(n2)
ellipse axes. So, we are using multi-dimensional balls defined by the centers
and radii. Calculation of statistical values like the mean and the average
deviation, can be done in an incremental way. While adding a point to a tree,
the statistical values for nodes recalculated in O(1) time. We support both,
brute force O(2n) and greedy O(n2) split algorithms. Statistical and aggregated
node information also allows to manipulate (to search, to delete) aggregated
sets of closely located points. Hierarchical information retrieval. When
searching, the user is provided with the highest appropriate nodes in the tree
hierarchy, with the most important clusters emerging in the hierarchy
automatically. Then, if interested, the user may navigate down the tree to more
specific points. The system is implemented as a library of Java classes
representing Points, Sets of points with aggregated statistical information,
B-tree, and Nodes with a support of serialization and storage in a MySQL
database.Comment: 6 pages with 3 example
A partitioning strategy for nonuniform problems on multiprocessors
The partitioning of a problem on a domain with unequal work estimates in different subddomains is considered in a way that balances the work load across multiple processors. Such a problem arises for example in solving partial differential equations using an adaptive method that places extra grid points in certain subregions of the domain. A binary decomposition of the domain is used to partition it into rectangles requiring equal computational effort. The communication costs of mapping this partitioning onto different microprocessors: a mesh-connected array, a tree machine and a hypercube is then studied. The communication cost expressions can be used to determine the optimal depth of the above partitioning
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