Graph, Tree and DAG Object and Window Structures
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Bayes Nets Conclusion Inference
Bayes Nets Conclusion Inference • Three sets of variables: • Query variables: E1 • Evidence variables: E2 • Hidden variables: The rest, E3 Cloudy P(W | Cloudy = True) • E = {W} Sprinkler Rain 1 • E2 = {Cloudy=True} • E3 = {Sprinkler, Rain} Wet Grass P(E 1 | E 2) = P(E 1 ^ E 2) / P(E 2) Problem: Need to sum over the possible assignments of the hidden variables, which is exponential in the number of variables. Is exact inference possible? 1 A Simple Case A B C D • Suppose that we want to compute P(D = d) from this network. A Simple Case A B C D • Compute P(D = d) by summing the joint probability over all possible values of the remaining variables A, B, and C: P(D === d) === ∑∑∑ P(A === a, B === b,C === c, D === d) a,b,c 2 A Simple Case A B C D • Decompose the joint by using the fact that it is the product of terms of the form: P(X | Parents(X)) P(D === d) === ∑∑∑ P(D === d | C === c)P(C === c | B === b)P(B === b | A === a)P(A === a) a,b,c A Simple Case A B C D • We can avoid computing the sum for all possible triplets ( A,B,C) by distributing the sums inside the product P(D === d) === ∑∑∑ P(D === d | C === c)∑∑∑P(C === c | B === b)∑∑∑P(B === b| A === a)P(A === a) c b a 3 A Simple Case A B C D This term depends only on B and can be written as a 2- valued function fA(b) P(D === d) === ∑∑∑ P(D === d | C === c)∑∑∑P(C === c | B === b)∑∑∑P(B === b| A === a)P(A === a) c b a A Simple Case A B C D This term depends only on c and can be written as a 2- valued function fB(c) === === === === === === P(D d) ∑∑∑ P(D d | C c)∑∑∑P(C c | B b)f A(b) c b …. -
ACE-2019-Query-Builder-And-Tree
Copyright © 2019 by Aras Corporation. This material may be distributed only subject to the terms and conditions set forth in the Open Publication License, V1.0 or later (the latest version is presently available at http://www.opencontent.org/openpub/). Distribution of substantively modified versions of this document is prohibited without the explicit permission of the copyright holder. Distribution of the work or derivative of the work in any standard (paper) book form for a commercial purpose is prohibited unless prior permission is obtained from the copyright holder. Aras Innovator, Aras, and the Aras Corp "A" logo are registered trademarks of Aras Corporation in the United States and other countries. All other trademarks referenced herein are the property of their respective owners. Microsoft, Office, SQL Server, IIS and Windows are either registered trademarks or trademarks of Microsoft Corporation in the United States and/or other countries. Notice of Liability The information contained in this document is distributed on an "As Is" basis, without warranty of any kind, express or implied, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose or a warranty of non-infringement. Aras shall have no liability to any person or entity with respect to any loss or damage caused or alleged to be caused directly or indirectly by the information contained in this document or by the software or hardware products described herein. Copyright © 2019 by Aras Corporation. This material may be distributed only subject to the terms and conditions set forth in the Open Publication License, V1.0 or later (the latest version is presently available at http://www.opencontent.org/openpub/). -
Tree Structures
Tree Structures Definitions: o A tree is a connected acyclic graph. o A disconnected acyclic graph is called a forest o A tree is a connected digraph with these properties: . There is exactly one node (Root) with in-degree=0 . All other nodes have in-degree=1 . A leaf is a node with out-degree=0 . There is exactly one path from the root to any leaf o The degree of a tree is the maximum out-degree of the nodes in the tree. o If (X,Y) is a path: X is an ancestor of Y, and Y is a descendant of X. Root X Y CSci 1112 – Algorithms and Data Structures, A. Bellaachia Page 1 Level of a node: Level 0 or 1 1 or 2 2 or 3 3 or 4 Height or depth: o The depth of a node is the number of edges from the root to the node. o The root node has depth zero o The height of a node is the number of edges from the node to the deepest leaf. o The height of a tree is a height of the root. o The height of the root is the height of the tree o Leaf nodes have height zero o A tree with only a single node (hence both a root and leaf) has depth and height zero. o An empty tree (tree with no nodes) has depth and height −1. o It is the maximum level of any node in the tree. CSci 1112 – Algorithms and Data Structures, A. -
A Simple Linear-Time Algorithm for Finding Path-Decompostions of Small Width
Introduction Preliminary Definitions Pathwidth Algorithm Summary A Simple Linear-Time Algorithm for Finding Path-Decompostions of Small Width Kevin Cattell Michael J. Dinneen Michael R. Fellows Department of Computer Science University of Victoria Victoria, B.C. Canada V8W 3P6 Information Processing Letters 57 (1996) 197–203 Kevin Cattell, Michael J. Dinneen, Michael R. Fellows Linear-Time Path-Decomposition Algorithm Introduction Preliminary Definitions Pathwidth Algorithm Summary Outline 1 Introduction Motivation History 2 Preliminary Definitions Boundaried graphs Path-decompositions Topological tree obstructions 3 Pathwidth Algorithm Main result Linear-time algorithm Proof of correctness Other results Kevin Cattell, Michael J. Dinneen, Michael R. Fellows Linear-Time Path-Decomposition Algorithm Introduction Preliminary Definitions Motivation Pathwidth Algorithm History Summary Motivation Pathwidth is related to several VLSI layout problems: vertex separation link gate matrix layout edge search number ... Usefullness of bounded treewidth in: study of graph minors (Robertson and Seymour) input restrictions for many NP-complete problems (fixed-parameter complexity) Kevin Cattell, Michael J. Dinneen, Michael R. Fellows Linear-Time Path-Decomposition Algorithm Introduction Preliminary Definitions Motivation Pathwidth Algorithm History Summary History General problem(s) is NP-complete Input: Graph G, integer t Question: Is tree/path-width(G) ≤ t? Algorithmic development (fixed t): O(n2) nonconstructive treewidth algorithm by Robertson and Seymour (1986) O(nt+2) treewidth algorithm due to Arnberg, Corneil and Proskurowski (1987) O(n log n) treewidth algorithm due to Reed (1992) 2 O(2t n) treewidth algorithm due to Bodlaender (1993) O(n log2 n) pathwidth algorithm due to Ellis, Sudborough and Turner (1994) Kevin Cattell, Michael J. Dinneen, Michael R. -
Kurzweil 1000 Version 12 New Features
Kurzweil 1000 Version 12 New Features For the most up-to-date feature information, refer to the Readme file on the product CD. The following is a summary of what’s new in Version 12. For complete details, go to the online Manual by pressing Alt+H+O. Where applicable, Search key words are provided for you to use in the online Manual. • While you may not notice any difference, the internal structure of Kurzweil 1000 Version 12 has been overhauled and now uses Microsoft .NET Framework. The intent is to make it easier for Cambium Learning Technologies to develop features for the product going forward. • Note that Kurzweil 1000 Version 12 now supports 64-bit operating systems and Microsoft Windows 7 operating system. • As always, Kurzweil 1000 has the latest OCR engines, FineReader 9.0.1 and ScanSoft 16.2. The new ScanSoft version includes recognition languages from the Sami family. • An especially exciting new feature is the New User Wizard, a set of topics that introduces and walks new users through a number of Kurzweil 1000 features and preference setups. It appears when you start up Kurzweil 1000, but can be disabled and accessed from the Help menu by pressing Alt+H+W. (Search: New User Wizard.) • Currency Recognition has been updated to support new bills. Note that Currency Recognition now requires a color scanner. • New features and enhancements in reference tools include: 1. updates of the American Heritage Dictionary and Roget’s Thesaurus. 2. the ability to find up to 114 of your previously looked up entries; and last but not least the addition to dictionary and thesaurus lookup of human pronunciations and Anagrams. -
A Mobile Interface for Navigating Hierarchical Information Space$
Journal of Visual Languages and Computing 31 (2015) 48–69 Contents lists available at ScienceDirect Journal of Visual Languages and Computing journal homepage: www.elsevier.com/locate/jvlc A mobile interface for navigating hierarchical information space$ Abhishek P. Chhetri a,n, Kang Zhang b,c, Eakta Jain c,d a Computer Engineering Program, Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, Richardson, TX 65080-3021, USA b School of Software Engineering, Tianjin University, Tianjin, China c Department of Computer Science, University of Texas at Dallas, Richardson, TX 65080-3021, USA d Texas Instruments, Dallas, TX, USA article info abstract Article history: This paper presents ERELT (Enhanced Radial Edgeless Tree), a tree visualization approach Received 2 June 2015 on modern mobile devices. ERELT is designed to offer a clear visualization of any tree Accepted 5 October 2015 structure with intuitive interaction. Such visualization can assist users in interacting with Available online 22 October 2015 a hierarchical structure such as a media collection, file system, etc. General terms: In the ERELT visualization, a subset of the tree is displayed at a time. The displayed tree Algorithms size depends on the maximum number of tree elements that can be put on the screen Design while maintaining clarity. Users can quickly navigate to the hidden parts of the tree Human factors through touch-based gestures. We have conducted a user study to evaluate this visuali- zation for a music collection. The study results show that this approach reduces the time Keywords: and effort in navigating tree structures for exploration and search tasks. -
Lowest Common Ancestors in Trees and Directed Acyclic Graphs1
Lowest Common Ancestors in Trees and Directed Acyclic Graphs1 Michael A. Bender2 3 Martín Farach-Colton4 Giridhar Pemmasani2 Steven Skiena2 5 Pavel Sumazin6 Version: We study the problem of finding lowest common ancestors (LCA) in trees and directed acyclic graphs (DAGs). Specifically, we extend the LCA problem to DAGs and study the LCA variants that arise in this general setting. We begin with a clear exposition of Berkman and Vishkin’s simple optimal algorithm for LCA in trees. The ideas presented are not novel theoretical contributions, but they lay the foundation for our work on LCA problems in DAGs. We present an algorithm that finds all-pairs-representative : LCA in DAGs in O~(n2 688 ) operations, provide a transitive-closure lower bound for the all-pairs-representative-LCA problem, and develop an LCA-existence algorithm that preprocesses the DAG in transitive-closure time. We also present a suboptimal but practical O(n3) algorithm for all-pairs-representative LCA in DAGs that uses ideas from the optimal algorithms in trees and DAGs. Our results reveal a close relationship between the LCA, all-pairs-shortest-path, and transitive-closure problems. We conclude the paper with a short experimental study of LCA algorithms in trees and DAGs. Our experiments and source code demonstrate the elegance of the preprocessing-query algorithms for LCA in trees. We show that for most trees the suboptimal Θ(n log n)-preprocessing Θ(1)-query algorithm should be preferred, and demonstrate that our proposed O(n3) algorithm for all- pairs-representative LCA in DAGs performs well in both low and high density DAGs. -
Trees a Tree Is a Graph Which Is (A) Connected and (B) Has No Cycles (Acyclic)
Trees A tree is a graph which is (a) Connected and (b) has no cycles (acyclic). 1 Lemma 1 Let the components of G be C1; C2; : : : ; Cr, Suppose e = (u; v) 2= E, u 2 Ci; v 2 Cj. (a) i = j ) !(G + e) = !(G). (b) i 6= j ) !(G + e) = !(G) − 1. (a) v u (b) u v 2 Proof Every path P in G + e which is not in G must contain e. Also, !(G + e) ≤ !(G): Suppose (x = u0; u1; : : : ; uk = u; uk+1 = v; : : : ; u` = y) is a path in G + e that uses e. Then clearly x 2 Ci and y 2 Cj. (a) follows as now no new relations x ∼ y are added. (b) Only possible new relations x ∼ y are for x 2 Ci and y 2 Cj. But u ∼ v in G + e and so Ci [ Cj becomes (only) new component. 2 3 Lemma 2 G = (V; E) is acyclic (forest) with (tree) components C1; C2; : : : ; Ck. jV j = n. e = (u; v) 2= E, u 2 Ci; v 2 Cj. (a) i = j ) G + e contains a cycle. (b) i 6= j ) G + e is acyclic and has one less com- ponent. (c) G has n − k edges. 4 (a) u; v 2 Ci implies there exists a path (u = u0; u1; : : : ; u` = v) in G. So G + e contains the cycle u0; u1; : : : ; u`; u0. u v 5 (a) v u Suppose G + e contains the cycle C. e 2 C else C is a cycle of G. C = (u = u0; u1; : : : ; u` = v; u0): But then G contains the path (u0; u1; : : : ; u`) from u to v – contradiction. -
Arxiv:1901.04560V1 [Math.CO] 14 Jan 2019 the flexibility That Makes Hypergraphs Such a Versatile Tool Complicates Their Analysis
MINIMALLY CONNECTED HYPERGRAPHS MARK BUDDEN, JOSH HILLER, AND ANDREW PENLAND Abstract. Graphs and hypergraphs are foundational structures in discrete mathematics. They have many practical applications, including the rapidly developing field of bioinformatics, and more generally, biomathematics. They are also a source of interesting algorithmic problems. In this paper, we define a construction process for minimally connected r-uniform hypergraphs, which captures the intuitive notion of building a hypergraph piece-by-piece, and a numerical invariant called the tightness, which is independent of the construc- tion process used. Using these tools, we prove some fundamental properties of minimally connected hypergraphs. We also give bounds on their chromatic numbers and provide some results involving edge colorings. We show that ev- ery connected r-uniform hypergraph contains a minimally connected spanning subhypergraph and provide a polynomial-time algorithm for identifying such a subhypergraph. 1. Introduction Graphs and hypergraphs provide many beautiful results in discrete mathemat- ics. They are also extremely useful in applications. Over the last six decades, graphs and their generalizations have been used for modeling many biological phenomena, ranging in scale from protein-protein interactions, individualized cancer treatments, carcinogenesis, and even complex interspecial relationships [12, 15, 16, 19, 22]. In the last few years in particular, hypergraphs have found an increasingly prominent position in the biomathematical literature, as they allow scientists and practitioners to model complex interactions between arbitrarily many actors [22]. arXiv:1901.04560v1 [math.CO] 14 Jan 2019 The flexibility that makes hypergraphs such a versatile tool complicates their analysis. Because of this, many different algorithms and metrics have been devel- oped to assist with their application [18, 23]. -
Completeview™ Video Client User Manual
CompleteView™ Video Client User Manual CompleteView™ Version 4.7 Contents Introduction ................................................................................................................ 1 End User License Agreement ........................................................................................................................1 System Requirements ....................................................................................................................................3 Operation .................................................................................................................... 3 Getting Started ...............................................................................................................................................3 Starting the CompleteView Client ................................................................................................................3 Logging In to the CompleteView Client .......................................................................................................4 The Login Dialog .........................................................................................................................................4 Client Application Update ...........................................................................................................................5 Contacting Video Servers ............................................................................................................................6 Application Overview -
Section 11.1 Introduction to Trees Definition
Section 11.1 Introduction to Trees Definition: A tree is a connected undirected graph with no simple circuits. : A circuit is a path of length >=1 that begins and ends a the same vertex. d d Tournament Trees A common form of tree used in everyday life is the tournament tree, used to describe the outcome of a series of games, such as a tennis tournament. Alice Antonia Alice Anita Alice Abigail Abigail Alice Amy Agnes Agnes Angela Angela Angela Audrey A Family Tree Much of the tree terminology derives from family trees. Gaea Ocean Cronus Phoebe Zeus Poseidon Demeter Pluto Leto Iapetus Persephone Apollo Atlas Prometheus Ancestor Tree An inverted family tree. Important point - it is a binary tree. Iphigenia Clytemnestra Agamemnon Leda Tyndareus Aerope Atreus Catreus Forest Graphs containing no simple circuits that are not connected, but each connected component is a tree. Theorem An undirected graph is a tree if and only if there is a unique simple path between any two of its vertices. Rooted Trees Once a vertex of a tree has been designated as the root of the tree, it is possible to assign direction to each of the edges. Rooted Trees g a e f e c b b d a d c g f root node a internal vertex parent of g b c d e f g leaf siblings h i a b c d e f g h i h i ancestors of and a b c d e f g subtree with b as its h i root subtree with c as its root m-ary trees A rooted tree is called an m-ary tree if every internal vertex has no more than m children. -
An Algorithm for the Exact Treedepth Problem James Trimble School of Computing Science, University of Glasgow Glasgow, Scotland, UK [email protected]
An Algorithm for the Exact Treedepth Problem James Trimble School of Computing Science, University of Glasgow Glasgow, Scotland, UK [email protected] Abstract We present a novel algorithm for the minimum-depth elimination tree problem, which is equivalent to the optimal treedepth decomposition problem. Our algorithm makes use of two cheaply-computed lower bound functions to prune the search tree, along with symmetry-breaking and domination rules. We present an empirical study showing that the algorithm outperforms the current state-of-the-art solver (which is based on a SAT encoding) by orders of magnitude on a range of graph classes. 2012 ACM Subject Classification Theory of computation → Graph algorithms analysis; Theory of computation → Algorithm design techniques Keywords and phrases Treedepth, Elimination Tree, Graph Algorithms Supplement Material Source code: https://github.com/jamestrimble/treedepth-solver Funding James Trimble: This work was supported by the Engineering and Physical Sciences Research Council (grant number EP/R513222/1). Acknowledgements Thanks to Ciaran McCreesh, David Manlove, Patrick Prosser and the anonymous referees for their helpful feedback, and to Robert Ganian, Neha Lodha, and Vaidyanathan Peruvemba Ramaswamy for providing software for the SAT encoding. 1 Introduction This paper presents a practical algorithm for finding an optimal treedepth decomposition of a graph. A treedepth decomposition of graph G = (V, E) is a rooted forest F with node set V , such that for each edge {u, v} ∈ E, we have either that u is an ancestor of v or v is an ancestor of u in F . The treedepth of G is the minimum depth of a treedepth decomposition of G, where depth is defined as the maximum number of vertices along a path from the root of the tree to a leaf.