arXiv subjects

Bhadrachalam Chitturi

Publications and source records attributed to Bhadrachalam Chitturi.

6 recordsLinked to original sources

arXiv2020

An Upper Bound for Sorting $R_n$ with LRE

A permutation $π$ over alphabet $Σ= {1,2,3,\ldots,n}$, is a sequence where every element $x$ in $Σ$ occurs exactly once. $S_n$ is the symmetric group consisting of all permutations of length $n$ defined over $Σ$. $I_n$ = $(1, 2, 3,\ldots, n)$ and $R_n =(n, n-1, n-2,\ldots, 2, 1)$ are identity (i.e. sorted) and reverse permutations respectively. An operation, that we call as an $LRE$ operation, has been defined in OEIS with identity A186752. This operation is constituted by three generators: left-rotation, right-rotation and transposition(1,2). We call transposition(1,2) that swaps the two leftmost elements as $Exchange$. The minimum number of moves required to transform $R_n$ into $I_n$ with $LRE$ operation are known for $n \leq 11$ as listed in OEIS with sequence number A186752. For this problem no upper bound is known. OEIS sequence A186783 gives the conjectured diameter of the symmetric group $S_n$ when generated by $LRE$ operations \cite{oeis}. The contributions of this article are: (a) The first non-trivial upper bound for the number of moves required to sort $R_n$ with $LRE$; (b) a tighter upper bound for the number of moves required to sort $R_n$ with $LRE$; and (c) the minimum number of moves required to sort $R_{10}$ and $R_{11}$ have been computed. Here we are computing an upper bound of the diameter of Cayley graph generated by $LRE$ operation. Cayley graphs are employed in computer interconnection networks to model efficient parallel architectures. The diameter of the network corresponds to the maximum delay in the network.

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arXiv2018

Sorting permutations with a transposition tree

The set of all permutations with $n$ symbols is a symmetric group denoted by $S_n$. A transposition tree, $T$, is a spanning tree over its $n$ vertices $V_T=${$1, 2, 3, \ldots n$} where the vertices are the positions of a permutation $π$ and $π$ is in $S_n$. $T$ is the operation and the edge set $E_T$ denotes the corresponding generator set. The goal is to sort a given permutation $π$ with $T$. The number of generators of $E_T$ that suffices to sort any $π\in S_n$ constitutes an upper bound. It is an upper bound, on the diameter of the corresponding Cayley graph $Γ$ i.e. $diam(Γ)$. A precise upper bound equals $diam(Γ)$. Such bounds are known only for a few trees. Jerrum showed that computing $diam(Γ)$ is intractable in general if the number of generators is two or more whereas $T$ has $n-1$ generators. For several operations computing a tight upper bound is of theoretical interest. Such bounds have applications in evolutionary biology to compute the evolutionary relatedness of species and parallel/distributed computing for latency estimation. The earliest algorithm computed an upper bound $f(Γ)$ in a $Ω(n!)$ time by examining all $π$ in $S_n$. Subsequently, polynomial time algorithms were designed to compute upper bounds or their estimates. We design an upper bound $δ^*$ whose cumulative value for all trees of a given size $n$ is shown to be the tightest for $n \leq 15$. We show that $δ^*$ is tightest known upper bound for full binary trees. Keywords: Transposition trees, Cayley graphs, permutations, sorting, upper bound, diameter, greedy algorithms.

cs.DS↗

arXiv2018

Minimum-Link Rectilinear Covering Tour is NP-hard in $R^{4}$

Given a set $P$ of $n$ points in $R^{d}$, a tour is a closed simple path that covers all the given points, i.e. a Hamiltonian cycle. % In $P$ if no three points are collinear then the points are said to be in general position. A \textit{link} is a line segment connecting two points and a rectilinear link is parallel to one of the axes. The problems of defining a path and a tour with minimum number of links, also known as Minimum-Link Covering Path and Minimum-Link Covering Tour respectively are proven to be NP-hard in $R^2$. The corresponding rectilinear versions are also NP-hard in $R^2$. A set of points is said to be in \textit{general position} for rectilinear versions of the problems if no two points share any coordinate. We call a set of points in $R^{d}$ to be in \textit{relaxed general position} if no three points share any coordinate and any two points can share at most one coordinate. That is, if the points are either in general position or in relaxed general position then an axis parallel line can contain at most one point. If points are in relaxed general position then these problems are NP-hard in $R^{10}$. We prove that these two problems are in fact NP-hard in $R^{4}$. If points in $R^{d},~d>1$ are in general position then the time complexities of these problems, both basic and rectilinear versions, are unknown.

cs.CC↗

arXiv2017

Distances in and Layering of a DAG

The diameter of an undirected unweighted graph $G=(V,E)$ is the maximum value of the distance from any vertex $u$ to another vertex $v$ for $u,v \in V$ where distance i.e. $d(u,v)$ is the length of the shortest path from $u$ to $v$ in $G$. DAG, is a directed graph without a cycle. We denote the diameter of an unweighted DAG $G=(V,E)$ by $δ(G)$. The stretch of a DAG $G$ is the length of longest path from $u$ to $v$ in $G$, for all choices of $(u, v) \in V$ denoted by $Δ(G)$. The diameter of an undirected graph can be computed in $O(|V|(|V|+|E|))$ time by executing breadth first search $|V|$ times. We show that stretch and diameter of a DAG can be computed in $O(|V|+|E|)$ time and $O(|V||E|)$ time respectively. A DAG is balanced if and only if a consistent assignment of level numbers to all vertices is possible. Layering refers to such an assignment. A balanced DAG is defined. An efficient algorithm that either detects whether a given DAG is unbalanced or layers it otherwise is designed with a running time of $O(|V|+|E|)$. \\ Key words: Diameter, directed acyclic graph, longest directed path, graph algorithms, complexity.

cs.DS↗

arXiv2017

Layered graphs: a class that admits polynomial time solutions for some hard problems

The independent set on a graph $G=(V,E)$ is a subset of $V$ such that no two vertices in the subset have an edge between them. The MIS problem on $G$ seeks to identify an independent set with maximum cardinality, i.e. maximum independent set or MIS. $V* \subseteq V$ is a vertex cover $G=(V,E)$ if every edge in the graph is incident upon at least one vertex in $V*$. $V* \subseteq V$ is dominating set of $G=(V,E)$ if forall $v \in V$ either $v \in V*$ or $\exists u \in V*$ and $(u,v) \in E$. A connected dominating set, CDS, is a dominating set that forms a single component in $G$. The MVC problem on $G$ seeks to identify a vertex cover with minimum cardinality, i.e. minimum vertex cover or MVC. Likewise, CVC seeks a connected vertex cover (CVC) with minimum cardinality. The problems MDS and CDS seek to identify a dominating set and a connected dominating set respectively of minimum cardinalities. MVC, CVC, MDS, and CDS on a general graph are known to be NP-complete. On certain classes of graphs they can be computed in polynomial time. Such algorithms are known for bipartite graphs, chordal graphs, cycle graphs, comparability graphs, claw-free graphs, interval graphs and circular arc graphs for some of these problems. In this article we introduce a new class of graphs called a layered graph and show that if the number of vertices in a layer is $O(\log \mid V \mid)$ then MIS, MVC, CVC, MDS and CDC can be computed in polynomial time. The restrictions that are employed on graph classes that admit polynomial time solutions for hard problems, e.g. lack of cycles, bipartiteness, planarity etc. are not applicable for this class. \\ Key words: Independent set, vertex cover, dominating set, dynamic programming, complexity, polynomial time algorithms.

cs.DS↗

arXiv2016

Adjacencies in Permutations

A permutation on an alphabet $ Σ$, is a sequence where every element in $ Σ$ occurs precisely once. Given a permutation $ π$= ($π_{1} $, $ π_{2} $, $ π_{3} $,....., $ π_{n} $) over the alphabet $ Σ$ =$\{ $0, 1, . . . , n$-$1 $\}$ the elements in two consecutive positions in $ π$ e.g. $ π_{i} $ and $ π_{i+1} $ are said to form an \emph{adjacency} if $ π_{i+1} $ =$ π_{i} $+1. The concept of adjacencies is widely used in computation. The set of permutations over $ Σ$ forms a symmetric group, that we call P$ _{n} $. The identity permutation, I$ _{n}$ $\in$ P$_{n}$ where I$_{n}$ =(0,1,2,...,n$-$1) has exactly n$ - $1 adjacencies. Likewise, the reverse order permutation R$_{n} (\in P_{n})$=(n$-$1, n$-$2, n$-$3, n$-$4, ...,0) has no adjacencies. We denote the set of permutations in P$_{n} $ with exactly k adjacencies with P$_{n} $(k). We study variations of adjacency. % A transposition exchanges adjacent sublists; when one of the sublists is restricted to be a prefix (suffix) then one obtains a prefix (suffix) transposition. We call the operations: transpositions, prefix transpositions and suffix transpositions as block-moves. A particular type of adjacency and a particular block-move are closely related. In this article we compute the cardinalities of P$_{n}$(k) i.e. $ \forall_k \mid $P$ _{n} $ (k) $ \mid $ for each type of adjacency in $O(n^2)$ time. Given a particular adjacency and the corresponding block-move, we show that $\forall_{k} \mid P_{n}(k)\mid$ and the expected number of moves to sort a permutation in P$_{n} $ are closely related. Consequently, we propose a model to estimate the expected number of moves to sort a permutation in P$_{n} $ with a block-move. We show the results for prefix transposition. Due to symmetry, these results are also applicable to suffix transposition.

cs.DM↗