scholarly journals On Compact Encoding of Pagenumber $k$ Graphs

2008 ◽  
Vol Vol. 10 no. 3 ◽  
Author(s):  
Cyril Gavoille ◽  
Nicolas Hanusse
Keyword(s):  
Lower Bound ◽  
Upper Bound ◽  
Constant Time ◽  
Worst Case ◽  
Encoding Scheme ◽  
Information Theoretic ◽  
Auxiliary Table ◽  
Minimum Number ◽  
International Audience

International audience In this paper we show an information-theoretic lower bound of kn - o(kn) on the minimum number of bits to represent an unlabeled simple connected n-node graph of pagenumber k. This has to be compared with the efficient encoding scheme of Munro and Raman of 2kn + 2m + o(kn+m) bits (m the number of edges), that is 4kn + 2n + o(kn) bits in the worst-case. For m-edge graphs of pagenumber k (with multi-edges and loops), we propose a 2mlog2k + O(m) bits encoding improving the best previous upper bound of Munro and Raman whenever m ≤ 1 / 2kn/log2 k. Actually our scheme applies to k-page embedding containing multi-edge and loops. Moreover, with an auxiliary table of o(m log k) bits, our coding supports (1) the computation of the degree of a node in constant time, (2) adjacency queries with O(logk) queries of type rank, select and match, that is in O(logk *minlogk / loglogm, loglogk) time and (3) the access to δ neighbors in O(δ) runs of select, rank or match;.

scholarly journals Partially Persistent Data Structures of Bounded Degree with Constant Update Time

1994 ◽  
Vol 1 (35) ◽  
Author(s):  
Gerth Stølting Brodal
Keyword(s):  
Lower Bound ◽  
Data Structures ◽  
Upper Bound ◽  
Constant Time ◽  
Machine Model ◽  
Worst Case ◽  
Bounded Degree ◽  
New Strategy ◽  
Persistent Data ◽  
Pebble Game

The problem of making bounded in-degree and out-degree data structures partially persistent is considered. The node copying method of Driscoll <em>et al.</em> is extended so that updates can be performed in <em>worst-case</em> constant time on the pointer machine model. Previously it was only known to be possible in amortised constant time [Driscoll89]. The result is presented in terms of a new strategy for Dietz and Raman's dynamic two player pebble game on graphs. It is shown how to implement the strategy, and the upper bound on the required number of pebbles is improved from 2b + 2d + O(sqrt(b)) to d + 2b, where b is the bound of the in-degree and d the bound of the out-degree. We also give a lower bound that shows that the number of pebbles depends on the out-degree d.

scholarly journals On the Minimum Number of Completely 3-Scrambling Permutations

2005 ◽  
Vol DMTCS Proceedings vol. AE,... (Proceedings) ◽  
Author(s):  
Jun Tarui
Keyword(s):  
Lower Bound ◽  
Upper Bound ◽  
Explicit Construction ◽  
Minimum Size ◽  
Minimum Number ◽  
International Audience

International audience A family $\mathcal{P} = \{\pi_1, \ldots , \pi_q\}$ of permutations of $[n]=\{1,\ldots,n\}$ is $\textit{completely}$ $k$-$\textit{scrambling}$ [Spencer, 1972; Füredi, 1996] if for any distinct $k$ points $x_1,\ldots,x_k \in [n]$, permutations $\pi_i$'s in $\mathcal{P}$ produce all $k!$ possible orders on $\pi_i (x_1),\ldots, \pi_i(x_k)$. Let $N^{\ast}(n,k)$ be the minimum size of such a family. This paper focuses on the case $k=3$. By a simple explicit construction, we show the following upper bound, which we express together with the lower bound due to Füredi for comparison. $\frac{2}{ \log _2e} \log_2 n \leq N^{\ast}(n,3) \leq 2\log_2n + (1+o(1)) \log_2 \log _2n$. We also prove the existence of $\lim_{n \to \infty} N^{\ast}(n,3) / \log_2 n = c_3$. Determining the value $c_3$ and proving the existence of $\lim_{n \to \infty} N^{\ast}(n,k) / \log_2 n = c_k$ for $k \geq 4$ remain open.

scholarly journals On the Average Case of MergeInsertion

2020 ◽  
Vol 64 (7) ◽  
pp. 1197-1224
Author(s):  
Florian Stober ◽  
Armin Weiß
Keyword(s):  
Lower Bound ◽  
Upper Bound ◽  
Sorting Algorithm ◽  
Worst Case ◽  
Average Case ◽  
Information Theoretic ◽  
Johnson Algorithm ◽  
The Impact ◽  
Combined Algorithm ◽  
Worst Case Behavior

AbstractMergeInsertion, also known as the Ford-Johnson algorithm, is a sorting algorithm which, up to today, for many input sizes achieves the best known upper bound on the number of comparisons. Indeed, it gets extremely close to the information-theoretic lower bound. While the worst-case behavior is well understood, only little is known about the average case. This work takes a closer look at the average case behavior. In particular, we establish an upper bound of $n \log n - 1.4005n + o(n)$ n log n − 1.4005 n + o ( n ) comparisons. We also give an exact description of the probability distribution of the length of the chain a given element is inserted into and use it to approximate the average number of comparisons numerically. Moreover, we compute the exact average number of comparisons for n up to 148. Furthermore, we experimentally explore the impact of different decision trees for binary insertion. To conclude, we conduct experiments showing that a slightly different insertion order leads to a better average case and we compare the algorithm to Manacher’s combination of merging and MergeInsertion as well as to the recent combined algorithm with (1,2)-Insertionsort by Iwama and Teruyama.

scholarly journals Encoding Two-Dimensional Range Top-k Queries

Algorithmica ◽  
2021 ◽  
Author(s):  
Seungbum Jo ◽  
Rahul Lingala ◽  
Srinivasa Rao Satti
Keyword(s):  
Lower Bound ◽  
Lower Bounds ◽  
Upper Bound ◽  
Total Order ◽  
Two Dimensional ◽  
Information Theoretic ◽  
Cartesian Tree ◽  
Dimensional Range

AbstractWe consider the problem of encoding two-dimensional arrays, whose elements come from a total order, for answering $${\text{Top-}}{k}$$ Top- k queries. The aim is to obtain encodings that use space close to the information-theoretic lower bound, which can be constructed efficiently. For an $$m \times n$$ m × n array, with $$m \le n$$ m ≤ n , we first propose an encoding for answering 1-sided $${\textsf {Top}}{\text {-}}k{}$$ Top - k queries, whose query range is restricted to $$[1 \dots m][1 \dots a]$$ [ 1 ⋯ m ] [ 1 ⋯ a ] , for $$1 \le a \le n$$ 1 ≤ a ≤ n . Next, we propose an encoding for answering for the general (4-sided) $${\textsf {Top}}{\text {-}}k{}$$ Top - k queries that takes $$(m\lg {{(k+1)n \atopwithdelims ()n}}+2nm(m-1)+o(n))$$ ( m lg ( k + 1 ) n n + 2 n m ( m - 1 ) + o ( n ) ) bits, which generalizes the joint Cartesian tree of Golin et al. [TCS 2016]. Compared with trivial $$O(nm\lg {n})$$ O ( n m lg n ) -bit encoding, our encoding takes less space when $$m = o(\lg {n})$$ m = o ( lg n ) . In addition to the upper bound results for the encodings, we also give lower bounds on encodings for answering 1 and 4-sided $${\textsf {Top}}{\text {-}}k{}$$ Top - k queries, which show that our upper bound results are almost optimal.

scholarly journals Stochastic Flips on Dimer Tilings

2010 ◽  
Vol DMTCS Proceedings vol. AM,... (Proceedings) ◽  
Author(s):  
Thomas Fernique ◽  
Damien Regnault
Keyword(s):  
Fixed Point ◽  
Markov Process ◽  
Upper Bound ◽  
Numerical Experiments ◽  
Triangular Grid ◽  
Expected Number ◽  
Worst Case ◽  
Average Case ◽  
International Audience

International audience This paper introduces a Markov process inspired by the problem of quasicrystal growth. It acts over dimer tilings of the triangular grid by randomly performing local transformations, called $\textit{flips}$, which do not increase the number of identical adjacent tiles (this number can be thought as the tiling energy). Fixed-points of such a process play the role of quasicrystals. We are here interested in the worst-case expected number of flips to converge towards a fixed-point. Numerical experiments suggest a $\Theta (n^2)$ bound, where $n$ is the number of tiles of the tiling. We prove a $O(n^{2.5})$ upper bound and discuss the gap between this bound and the previous one. We also briefly discuss the average-case.

scholarly journals m-Bonsai: A Practical Compact Dynamic Trie

2018 ◽  
Vol 29 (08) ◽  
pp. 1257-1278 ◽  
Author(s):  
Andreas Poyias ◽  
Simon J. Puglisi ◽  
Rajeev Raman
Keyword(s):  
Data Structure ◽  
Lower Bound ◽  
Upper Bound ◽  
Hash Functions ◽  
Information Theoretic ◽  
Expected Time ◽  
Speed Performance ◽  
Practical Performance

We consider the problem of implementing a space-efficient dynamic trie, with an emphasis on good practical performance. For a trie with [Formula: see text] nodes with an alphabet of size [Formula: see text], the information-theoretic space lower bound is [Formula: see text] bits. The Bonsai data structure is a compact trie proposed by Darragh et al. (Softw. Pract. Exper. 23(3) (1993) 277–291). Its disadvantages include the user having to specify an upper bound [Formula: see text] on the trie size in advance (which cannot be changed easily after initalization), a space usage of [Formula: see text] (which is asymptotically non-optimal for smaller [Formula: see text] or if [Formula: see text]) and a lack of support for deletions. It supports traversal and update operations in [Formula: see text] expected time (based on assumptions about the behaviour of hash functions), where [Formula: see text] and has excellent speed performance in practice. We propose an alternative, m-Bonsai, that addresses the above problems, obtaining a trie that uses [Formula: see text] bits in expectation, and supports traversal and update operations in [Formula: see text] expected time and [Formula: see text] amortized expected time, for any user-specified parameter [Formula: see text] (again based on assumptions about the behaviour of hash functions). We give an implementation of m-Bonsai which uses considerably less memory and is slightly faster than the original Bonsai.

scholarly journals Partial Quicksort and Quickpartitionsort

2010 ◽  
Vol DMTCS Proceedings vol. AM,... (Proceedings) ◽  
Author(s):  
Conrado Martínez ◽  
Uwe Rösler
Keyword(s):  
Lower Bound ◽  
Time Analysis ◽  
Information Theoretic ◽  
Running Time ◽  
Running Time Analysis ◽  
International Audience

International audience Partial Quicksort sorts the $l$ smallest elements in a list of length $n$. We provide a complete running time analysis for this combination of Find and Quicksort. Further we give some optimal adapted versions, called Partition Quicksort, with an asymptotic running time $c_1l\ln l+c_2l+n+o(n)$. The constant $c_1$ can be as small as the information theoretic lower bound $\log_2 e$.

scholarly journals Local chromatic number and topology

2005 ◽  
Vol DMTCS Proceedings vol. AE,... (Proceedings) ◽  
Author(s):  
Gábor Simonyi ◽  
Gábor Tardos
Keyword(s):  
Lower Bound ◽  
Chromatic Number ◽  
Topological Method ◽  
Proper Coloring ◽  
And Topology ◽  
Minimum Number ◽  
International Audience ◽  
Closed Neighborhood

International audience The local chromatic number of a graph, introduced by Erdős et al., is the minimum number of colors that must appear in the closed neighborhood of some vertex in any proper coloring of the graph. This talk would like to survey some of our recent results on this parameter. We give a lower bound for the local chromatic number in terms of the lower bound of the chromatic number provided by the topological method introduced by Lovász. We show that this bound is tight in many cases. In particular, we determine the local chromatic number of certain odd chromatic Schrijver graphs and generalized Mycielski graphs. We further elaborate on the case of $4$-chromatic graphs and, in particular, on surface quadrangulations.

scholarly journals On Bipartite Distinct Distances in the Plane

10.37236/9687 ◽  
2021 ◽  
Vol 28 (4) ◽  
Author(s):  
Surya Mathialagan
Keyword(s):  
Lower Bound ◽  
Upper Bound ◽  
Crossing Number ◽  
Minimum Number

Given sets $\mathcal{P}, \mathcal{Q} \subseteq \mathbb{R}^2$ of sizes $m$ and $n$ respectively, we are interested in the number of distinct distances spanned by $\mathcal{P} \times \mathcal{Q}$. Let $D(m, n)$ denote the minimum number of distances determined by sets in $\mathbb{R}^2$ of sizes $m$ and $n$ respectively, where $m \leq n$. Elekes showed that $D(m, n) = O(\sqrt{mn})$ when $m \leqslant n^{1/3}$. For $m \geqslant n^{1/3}$, we have the upper bound $D(m, n) = O(n/\sqrt{\log n})$ as in the classical distinct distances problem.In this work, we show that Elekes' construction is tight by deriving the lower bound of $D(m, n) = \Omega(\sqrt{mn})$ when $m \leqslant n^{1/3}$. This is done by adapting Székely's crossing number argument. We also extend the Guth and Katz analysis for the classical distinct distances problem to show a lower bound of $D(m, n) = \Omega(\sqrt{mn}/\log n)$ when $m \geqslant n^{1/3}$.

scholarly journals Low Redundancy in Dictionaries with O(1) Worst Case Lookup Time

1998 ◽  
Vol 5 (28) ◽  
Author(s):  
Rasmus Pagh
Keyword(s):  
Data Structure ◽  
Unit Cost ◽  
Query Time ◽  
Constant Time ◽  
Worst Case ◽  
Insertions And Deletions ◽  
Word Size ◽  
Membership Queries ◽  
Dynamic Setting ◽  
Minimum Number

A static dictionary is a data structure for storing subsets of a finite universe U, so that membership queries can be answered efficiently. We study this problem in a unit cost RAM model with word size  Omega(log |U|), and show that for n-element subsets,<br />constant worst case query time can be obtained using B +O(log log |U|)+o(n) bits of storage, where B = [log2 (|U| / n)]<br />is the minimum number of bits needed to represent all<br />such subsets. The solution for dense subsets uses B + O( |U| log log |U| / log |U| ) bits of storage, and supports constant time rank queries. In a dynamic setting, allowing insertions and deletions, our techniques give an O(B) bit space usage.

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