On embedding an outer-planar graph in a point set

This paper starts the investigation of a constrained version of the point-set embed-dability problem. Let G = (V,E) be a planar graph with n vertices, G′ = (V′,E′) a subgraph of G, and S a set of n distinct points in the plane. We study the problem of computing a point-set embedding of G on S subject to the constraint that G′ is drawn with straight-line edges. Different drawing algorithms are presented that guarantee small curve complexity of the resulting drawing, i.e. a small number of bends per edge. It is proved that: • If G′ is an outerplanar graph and S is any set of points in convex position, a point-set embedding of G on S can be computed such that the edges of E\E′ have at most 4 bends each. • If S is any set of points in general position and G′ is a face of G or if it is a simple path, the curve complexity of the edges of E\E′ is at most 8. • If S is in general position and G′ is a set of k disjoint paths, the curve complexity of the edges of E \ E′ is O(2k).

Given an n-vertex outerplanar graph G, the problem is considered of arranging the vertices of G on a line so that no two edges cross and various cost measures are minimized. Efficient algorithms are presented for generating layouts in which every edge (i, j) and of G does not exceed a given bandwidth b(i, j), and the total edge length and the cutwidth of the layout are minimized. Characterizations of optimal layouts used by the algorithms are given. The algorithms combine sublayouts by solving two processor-scheduling problems. Although these scheduling problems are generally NP-complete, the instances generated by the algorithms are polynomial in n. >

Let be a class of probability distributions over the discrete domain [n] = {1, …, n}. We show that if satisfies a rather general condition – essentially, that each distribution in can be well-approximated by a variable-width histogram with few bins – then there is a highly efficient (both in terms of running time and sample complexity) algorithm that can learn any mixture of k unknown distributions from . We analyze several natural types of distributions over [n], including log-concave, monotone hazard rate and unimodal distributions, and show that they have the required structural property of being well-approximated by a histogram with few bins. Applying our general algorithm, we obtain near-optimally efficient algorithms for all these mixture learning problems as described below. More precisely, Log-concave distributions: We learn any mixture of k log-concave distributions over [n] using k · Õ(1/ε4) samples (independent of n) and running in time Õ(k log(n)/ε4) bit-operations (note that reading a single sample from [n] takes Θ(log n) bit operations). For the special case k = 1 we give an efficient algorithm using Õ(1/ε3) samples; this generalizes the main result of [DDS12b] from the class of Poisson Binomial distributions to the much broader class of all log-concave distributions. Our upper bounds are not far from optimal since any algorithm for this learning problem requires Ω(k/ε5/2) samples. Monotone hazard rate (MHR) distributions: We learn any mixture of k MHR distributions over [n] using O(k log(n/ε)/ε4) samples and running in time Õ(k log (n)/ε4) bit-operations. Any algorithm for this learning problem must use Ω(k log(n)/ε3) samples. Unimodal distributions: We give an algorithm that learns any mixture of k unimodal distributions over [n] using O(k log(n)/ε4) samples and running in time Õ(k log2(n)/ε4) bit-operations. Any algorithm for this problem must use Ω(k log(n)/ε3) samples.

Determination of minimum number of sensors and their locations for an automated facility: An algorithmic approach

A directed graph D is semicomplete if for every pair x, y of vertices of D, there is at least one arc between x and y. Thus, a tournament is a semicomplete digraph. In the Directed Component Order Connectivity (DCOC) problem, given a digraph D=(V,A) and a pair of natural numbers k and ell , we are to decide whether there is a subset X of V of size k such that the largest strongly connected component in D-X has at most ell vertices. Note that DCOC reduces to the Directed Feedback Vertex Set problem for ell =1. We study the parameterized complexity of DCOC for general and semicomplete digraphs with the following parameters: k, ell ,ell +k and n-ell . In particular, we prove that DCOC with parameter k on semicomplete digraphs can be solved in time O^*(2^{16k}) but not in time O^*(2^{o(k)}) unless the Exponential Time Hypothesis (ETH) fails. The upper bound O^*(2^{16k}) implies the upper bound O^*(2^{16(n-ell )}) for the parameter n-ell . We complement the latter by showing that there is no algorithm of time complexity O^*(2^{o({n-ell })}) unless ETH fails. Finally, we improve (in dependency on ell ) the upper bound of Göke, Marx and Mnich (2019) for the time complexity of DCOC with parameter ell +k on general digraphs from O^*(2^{O(kell log (kell ))}) to O^*(2^{O(klog (kell ))}). Note that Drange, Dregi and van ’t Hof (2016) proved that even for the undirected version of DCOC on split graphs there is no algorithm of running time O^*(2^{o(klog ell )}) unless ETH fails and it is a long-standing problem to decide whether Directed Feedback Vertex Set admits an algorithm of time complexity O^*(2^{o(klog k)}).

Big data is technology that has the ability to manage very large amounts of data, in very fast time to allow real-time analysis and reactions. Several clustering methods which are used to group data are Fuzzy C-Means (FCM) and K-Means Clustering. K-Means Clustering algorithm is a method of partitioning existing data into two or more group. This research goal was to compare the performance of K-Means and Fuzzy C-Means algorithms in clustering data using big data technology. In this research, Hadoop and Hive were chosen the big data technology. The knowledge of Shia history on student and lecturer of Syarif Hidayatullah State Islamic University Jakarta were the data which used in this research, The testing was done by constructing application K-Means Fuzzy C-Means using Java language, Hadoop and Hive and then test the performance of K-Means and Fuzzy C-Means algorithms in data clustering. It compares both algorithms in terms of accuracy, execution time, and time complexity of the algorithms. In the application K-Means Fuzzy C-Means, evaluation were performed with data filter and the average accuracy difference result of K-Means and Fuzzy C-Means is 8.03% with the better accuracy owned by K-Means. The average execution time difference is 718.58 ms, which K-Means was faster than is Fuzzy C-Means. The time complexities of both algorithms have the same value O(n2) and the Big O equation resulted in an average difference of 93,568 with the smallest value on K-Means. Thus, K-Means algorithm is better than the Fuzzy C-Means in terms of accuracy, execution time, and the time complexity.

We consider a network facility location problem with unbounded production levels. This problem is NP-hard in the general case and is known to have an optimal solution with quadratic complexity on a tree network. We study the case of a network representable by an outerplanar graph, i.e., by a graph whose vertices belong to one (outer) face. This problem is known to have an optimal algorithm with time complexity O(nm3), where n is the number of vertices and m is the number of possible facility locations. Using some properties of outerplanar graphs (binary 2-trees) and the existence of an optimal solution with a family of centrally connected service areas, we obtain recurrence relations for the construction of an optimal algorithm with time complexity that is smaller by a factor of \(\sqrt m \) than the time complexity of the earlier algorithm.

We present a new game, Dots & Polygons, played on a planar point set. We prove that its NP-hard and discuss strategies for the case when the point set is in convex position.

Transportation problems are one of the particular forms that often appear in linear programs, one of which is the distribution of goods. A transportation method is needed to determine the optimal result, namely, the minimum cost from source to destination with all demand and supply fulfilled. There are several methods, one of which is the Lowest Supply Lowest Cost Method (LSLC) and the Exponential Approach Method (PE). Both methods are made in a MATLAB program, generating a script that calculates the algorithm's time complexity. Using the function notation, the Big-O Algorithm complexity of the Lowest Supply Lowest Cost method is more efficient than the Exponential Approach Method algorithm. At the same time, the optimal result for the minimum cost between the two methods is obtained by using the Exponential Approach Method.

An outerplanar graph is a planar graph that can be embedded in the plane in such a way that all vertices lie on the outer boundary. Outerplanar graphs express many chemical compounds. An externally extensible outerplanar graph pattern (eeo-graph pattern for short) represents a graph pattern common to a finite set of outerplanar graphs, like a dataset of chemical compounds. The eeo-graph pattern can express a substructure common to blocks that appear in outerplanar graph structured data. In this paper, we propose a polynomial time algorithm for deciding whether or not a given eeo-graph pattern matches a given connected outerplanar graph. Moreover, we show the expressiveness of the pattern class by experiments on a chemical compound database.

In this paper we present some efficient and a few optimal algorithms to compute the radius, diameter, incenter, circumcenter, width and k-dimensional enclosing cylinder for convex polyhedral and convex polyhedral offset distance functions in plane and in \(\mathfrak {R}^d\). The radius, incenter and circumcenter of a convex polygon can be optimally computed in linear time, i.e. \(O(n+m)\), in \(\mathfrak {R}^2\) and in time \(O(n+m+|R|)\), if the incenter or circumcenter is additionally constrained in a convex polygon R, where n is the size of input polygon and m is the size of polygon in convex polygonal or convex polygonal offset distance functions. In \(\mathfrak {R}^d\), the radius, incenter and circumcenter of a convex polyhedron as well as of a set of convex polyhedra, unconstrained or constrained, can be computed in O(nm) and \(O(nm+|R|)\) time respectively, for convex constraint polyhedron R. The diameter of a convex polygon in plane can be computed in \(O(n+m)\) time. The diameter of a polyhedron in \(\mathfrak {R}^d\) can be computed in O(nm) time by our methods. The width of a convex polyhedron can be computed in \(O(n + m)\) time and O(nm) time, for \(\mathfrak {R}^2\) and \(\mathfrak {R}^d\), respectively. The diameter and width of a set of convex polyhedra can be computed in O(nm)-time. We also show how the k-dimensional minimum enclosing cylinder can be computed in \(O(n^{d-k+1}nm^2)\) in \(\mathfrak {R}^d\) and in \(O(n^{d}m(n+m))\) in \(\mathfrak {R}^d\) for \(k=1\). The k-dimensional minimum enclosing cylinder for a set of convex polyhedra in \(\mathfrak {R}^d\) can be computed in \(O(n^{d-k+1}nm^2)\)-time. The diameter and width problems can also be solved for a set of points in \(\mathfrak {R}^d\), either in time O(nm) or in time O(mT(n)), where T(n) is the time complexity of the best convex hull computation algorithm for the given set of points. We also compute the minimum stabbing sphere for a set of convex polyhedra, unconstrained or constrained, for the above mentioned distance functions in O(nm)-time or \(O(nm+|R|)\)-time respectively for convex constraint polyhedron R.

An efficient multiple meta-model-based global optimization method for computationally intensive problems

The complexity of the maximum common subgraph problem in partial k-trees is still largely unknown. We consider the restricted case, where the input graphs are k-connected partial k-trees and the common subgraph is required to be k-connected. For biconnected outerplanar graphs this problem is solved and the general problem was reported to be tractable by means of tree decomposition techniques. We discuss key obstacles of tree decompositions arising for common subgraph problems that were ignored by previous algorithms and do not occur in outerplanar graphs. We introduce the concept of potential separators, i.e., separators of a subgraph to be searched that not necessarily are separators of the input graph. We characterize these separators and propose a polynomial time solution for series-parallel graphs based on SP-trees.KeywordsTree decompositionmaximum common subgraphseries-parallel graph

This paper describes the theory and algorithms of fuzzy distance transform (FDT). Fuzzy distance is defined as the length of the shortest path between two points. The length of a path in a fuzzy subset is defined as the integration of fuzzy membership values of the points along the path. The shortest path between two points is the one with the minimum length among all (infinitely many) paths between the two points. It is demonstrated that, unlike in the binary case, the shortest path in a fuzzy subset is not necessarily a straight-line segment. The support of a fuzzy subset is the set of points with nonzero membership values. It is shown that, for any fuzzy subset, fuzzy distance is a metric for the interior of its support. FDT is defined as the process on a fuzzy subset that assigns at each point the smallest fuzzy distance from the boundary of the support. The theoretical framework of FDT in continuous space is extended to digital spaces and a dynamic programming-based algorithm is presented for its computation. Several potential medical imaging applications are presented including the quantification of blood vessels and trabecular bone thickness in the regime of limited special resolution.

Production matrices for geometric graphs