TL;DR: A tree is a graph with one and only one path between every two nodes, where at least one path exists between any two nodes and the length of each branch is given.
Abstract: We consider n points (nodes), some or all pairs of which are connected by a branch; the length of each branch is given. We restrict ourselves to the case where at least one path exists between any two nodes. We now consider two problems. Problem 1. Constrnct the tree of minimum total length between the n nodes. (A tree is a graph with one and only one path between every two nodes.) In the course of the construction that we present here, the branches are subdivided into three sets: I. the branches definitely assignec~ to the tree under construction (they will form a subtree) ; II. the branches from which the next branch to be added to set I, will be selected ; III. the remaining branches (rejected or not yet considered). The nodes are subdivided into two sets: A. the nodes connected by the branches of set I, B. the remaining nodes (one and only one branch of set II will lead to each of these nodes), We start the construction by choosing an arbitrary node as the only member of set A, and by placing all branches that end in this node in set II. To start with, set I is empty. From then onwards we perform the following two steps repeatedly. Step 1. The shortest branch of set II is removed from this set and added to
Problem 1. Construct the tree of minimum total length between the n nodes.
(A tree is a graph with one and only one path between every two nodes.).
In the course ol the construction that the authors present here, the branches are subdivided into three sets: I. the branches definitely assigned to the tree under construction (they will form a subtree) ; IL the branches from which the next branch to be added to set I, will be selected; III.
The rernaining branches (rejected or not yet considered)'.
The nodes are subdivided into two sets: A. the nodes connected by the branches of set I, B. the remaining nodes (one and only one branch of set II will lead to each of these nodes).
From then onwalals the authors Pedorm the following two steps repeatedly.
TL;DR: A set of automated procedures for obtaining accurate reconstructions of the cortical surface are described, which have been applied to data from more than 100 subjects, requiring little or no manual intervention.
Abstract: Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging. r 1999 Academic Press
9,599 citations
Cites methods from "A note on two problems in connexion..."
...A simpler technique that is computationally tractable is to take a dynamic programming approach to the calculation of distances and employ an algorithm typically used to calculate minimal distances in a graph ( Dijkstra, 1959 )....
TL;DR: This text introduces the basic data structures and programming techniques often used in efficient algorithms, and covers use of lists, push-down stacks, queues, trees, and graphs.
Abstract: From the Publisher:
With this text, you gain an understanding of the fundamental concepts of algorithms, the very heart of computer science. It introduces the basic data structures and programming techniques often used in efficient algorithms. Covers use of lists, push-down stacks, queues, trees, and graphs. Later chapters go into sorting, searching and graphing algorithms, the string-matching algorithms, and the Schonhage-Strassen integer-multiplication algorithm. Provides numerous graded exercises at the end of each chapter.
0201000296B04062001
TL;DR: Platform-independent and open source igraph aims to satisfy all the requirements of a graph package while possibly remaining easy to use in interactive mode as well.
Abstract: There is no other package around that satisfies all the following requirements: •Ability to handle large graphs efficiently •Embeddable into higher level environments (like R [6] or Python [7]) •Ability to be used for quick prototyping of new algorithms (impossible with “click & play” interfaces) •Platform-independent and open source igraph aims to satisfy all these requirements while possibly remaining easy to use in interactive mode as well.
8,850 citations
Cites background from "A note on two problems in connexion..."
...Dealing with large graphs can be different though – if it takes three months to calculate the diameter of a graph, nobody wants that to be interactive....
TL;DR: In this paper, the authors present Graph Theory with Applications: Graph theory with applications, a collection of applications of graph theory in the field of Operational Research and Management. Journal of the Operational research Society: Vol. 28, Volume 28, issue 1, pp. 237-238.
Abstract: (1977). Graph Theory with Applications. Journal of the Operational Research Society: Vol. 28, Volume 28, issue 1, pp. 237-238.
TL;DR: This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms, into planning under differential constraints that arise when automating the motions of virtually any mechanical system.
Abstract: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.
6,340 citations
Cites methods from "A note on two problems in connexion..."
...The result is the well-known Dijkstra’s algorithm for finding single-source shortest paths in a graph [275], which is a special form of dynamic programming....
TL;DR: Kurosh and Levitzki as discussed by the authors, on the radical of a general ring and three problems concerning nil rings, Bull Amer Math Soc vol 49 (1943) pp 913-919 10 -, On the structure of algebraic algebras and related rings.
Abstract: 7 A Kurosh, Ringtheoretische Probleme die mit dem Burnsideschen Problem uber periodische Gruppen in Zussammenhang stehen, Bull Acad Sei URSS, Ser Math vol 5 (1941) pp 233-240 8 J Levitzki, On the radical of a general ring, Bull Amer Math Soc vol 49 (1943) pp 462^66 9 -, On three problems concerning nil rings, Bull Amer Math Soc vol 49 (1943) pp 913-919 10 -, On the structure of algebraic algebras and related rings, Trans Amer Math Soc vol 74 (1953) pp 384-409
TL;DR: The labeling algorithm for the solution of maximal network flow problems and its application to various problems of the transportation type are discussed.
Abstract: : The labeling algorithm for the solution of maximal network flow problems and its application to various problems of the transportation type are discussed.
TL;DR: Two methods for systematically selecting the shortest connections from a list of possible connections to obtain a minimum total wire length are presented, which will be called a minimum tree.
Abstract: In the construction of a digital computer in which high-frequency circuitry is used, it is desirable and often necessary when making connections between terminals to minimize the total wire length in order to reduce the capacitance and delay-line effects of long wire leads. Presented here are two methods for systematically selecting the shortest connections from a list of possible connections to obtain a minimum total wire length. The special problem considered here is the following: Given a number of terminals, fixed in space, which must be electrically connected together, what procedure will provide the minimum wire length? A proper pattern of connections is one in which there exists one and only one path, either direct or through other terminals, from each terminal to every other terminal and in which there are no loops created by redundant connections. Figure 1 shows two possible proper patterns for connecting four terminals. Problems of this type have been considered in topological areas of mathematics , more particularly in the theory of graphs [1]. In accordance with the prevailing terminology, the following definitions will be used hereafter in this paper. A terminal either connected or mlconnected will be referred to as a node. The direct connection between two nodes is a branch, the magnitude of which is the distance between the nodes. A path between two nodes is a connection consisting of one or more branches. A graph is a structure of nodes connected pairwise by one or more branches. A tree is a graph having one and only one path between every two nodes. It has previously been referred to as a proper pattern. A minimum proper pattern, i.e., a proper pattern where the sum of the wire lengths is a minimum, will be called a minimum tree. A subtree is a tree comprising k of n nodes where k < n. To connect electrically n nodes into a tree, exactly (n-1) branches are necessary [1]. If more than (n-1) branches are used there will be redundant connections and loops will be formed. If less than (n-1) branches are used, not all of the nodes will be interconnected. If exactly (n-1) branches are used, but incorrectly , both loops and unconnected nodes result. To produce a minimum tree, it is conceivably possible to investigate all of the possible trees that exist for n nodes. It can be shown [2, 3] that the number of …
Q1. What have the authors contributed in "A note on two problems in connexion with graphs" ?
In the course ol the construction that the authors present here, the branches are subdivided into three sets: I. the branches definitely assigned to the tree under construction ( they will form a subtree ) ; IL the branches from which the next branch to be added to set I, will be selected ; III. From then onwalals the authors Pedorm the following two steps repeatedly.