Octree-Based Projection Mesh Generation for Complex 'Dirty' Geometries

Total Page:16

File Type:pdf, Size:1020Kb

Octree-Based Projection Mesh Generation for Complex 'Dirty' Geometries Octree-based Projection Mesh Generation for Complex 'Dirty' Geometries ALEXANDER V. SHEVELKO A thesis submitted in partial fulfilment of the requirements of the University of Brighton for the degree of Doctor of Philosophy April 2006 The University of Brighton in collaboration with Ricardo UK Limited Abstract The problem of robust fully automatic 3D body-fitted mesh generation for industrial geometries is considered. A new projection-based method is developed for a general-type unstructured solver. Unlike other projection techniques, it starts with generation of a high quality prismatic dummy boundary layer. The nodes are gradually moved towards the domain boundaries and important geometric features are resolved. The minimum mesh quality is preserved at every stage of the process. This guarantees that the obtained grid is valid for calculations. The algorithm uses octree data structure as a base mesh. Its implementation is described in detail. Geometry localisation and the refinement criteria for 'dirty' geometries are further developed. The method starts with triangulated geometric boundaries. The quality of the input data can be extremely poor. A problem of leaking of the base mesh is considered. Several strategies to overcome it are proposed. The generated polyhedral mesh can also be used for fully automatic tetrahedral or hybrid grid generation. Near boundary prismatic cells can be anisotropically refined for boundary layer mesh generation for viscous flow calculations. The developed methodology can also be used for polygonal, triangular or hybrid surface mesh generation and shrink-wrapping to achieve the following basic aims: 'cleaning' of 'dirty' geometries, reduction of model elements, generation of external surfaces, thickening and offset of geometries. The spatial and surface mesh generation and shrink-wrapping are implemented as a computer program. User intervention can be reduced to a setting of several control parameters leading to a fully automatic mesh generation process. Examples of generated meshes and calculations for several engineering models are given. 11 Dedication This thesis is dedicated to my deeply beloved friend Lesya (24 April 1990 - 25 September 2004), whom I miss dearly ... iii Table of Contents ABSTRACT ............................................................................................................................................... 11 DEDICATION ......................................................................................................................................... III TABLE OF CONTENTS ........................................................................................................................ IV LIST OF FIGURES ................................................................................................................................. VI LIST OF TABLES ..................................................................................................................................... X ACKNOWLEDGMENTS ....................................................................................................................... XI DECLARATION .................................................................................................................................... XII ABBREVIATIONS ............................................................................................................................... XIII CHAPTER 1. INTRODUCTION .............................................................................................................. ! 1.1 BACKGROUND .................................................................................................................................... 1 1.2 THE AIM OF THE THESIS .............................................................. ...................................................... .4 1.3 CONTRIBUTION ................................................................................................................................... 5 1.4 THESIS OUTLINE ................................................................................................................................. 6 CHAPTER 2. LITERATURE REVIEW .................................................................................................. 8 2.1 INTRODUCTION ................................................................................................................................... 8 2.2 MESH REQUIREMENTS ........................................................................................................................ 9 2.3 MESH CHARACTERISTICS .................................................................................................................. 11 2.4 MESH CLASSIFICATION ..................................................................................................................... 14 2.5 CLASSIFICATION OF MESH GENERATION METHODS ......................................................................... 17 2.5.1 Parametric Methods .... .... ........... ................................................................ ..... ...... ............ .... ... 17 2.5.2 Insertion I Creation Methods ........... .... ........................... .... ...... ... ............................... .. ........... 18 2.5.3 Spatial Decomposition and Hybrid Methods ........................................................ .... ........ .. ..... 18 2.6 CLASSIFICATION OF CARTESIAN-BASED METHODS ........................................................................... 19 2.7 NON-BODY-FITTED CARTESIAN-BASED METHODS ............................................................................ 20 2. 7.1 Stair-cased Methods ....... .................................................. ........................................................ 20 2. 7.2 Cut-cell Methods .............................. ...... ..... ...... ....... ... ... ...................... ............ ...... ........ ..... .. ... 21 2. 7. 3 Treatments ofthe Small Cell Problem ............ ........ .. .................... .. ............... ................ .......... 24 2. 7. 4 MC-based Methods ... ..... ... ... .... .. ...... .. ........ .. .......................................... ...... ............ ................ 26 2.8 HYBRID CARTESIAN-BASED METHODS ............................................................................................. 29 2. 8.1 Structured/Unstructured Methods ............................ ................................................................ 29 2.8.2 Overset Methods .... ....... ........ .................. .............................. .. ....... .. .... .............. .. ...... .............. 31 2.9 PROJECTION-BASED METHODS ......................................................................................................... 32 2. 9.1 Modified-Quadtree/Octree Method .. ..... ........... .... .... ... .......... .......................... ...... ................... 33 2.9.2 Multiple Projections Methods .................... ....................... .......................... ..... .... .. ........ ... .... ... 34 2.9.3 Minimum Distance Methods ................................................ .... ... ................... ...... ... ................. 37 2.9.4 Hybrid Projection Methods .............. ............ ........... .... ... .... ...................................................... 40 2.10 CONCLUSIONS TO CHAPTER 2 ........................................................................................................ .41 CHAPTER 3. OCTREE MESH GENERATION .................................................................................. 44 3.1 INTRODUCTION ................................................................................................................................ .44 3.2 OCTREE DOMAIN DECOMPOSITION ................................................................................................... 45 3.2.1 Basic Notions .................................................... ................................... .. ......... ..... ... ................. 45 3.2.2 Octree Data Structure ....................... .. ..... ... ..... ............... .... ........................... ........... ............... 47 3.2.3 Tree Operations .................................... ........... ................... .................... .. .......... .... .......... ....... 50 3.3 GEOMETRY LOCALISATION ............................................................................................................... 53 3. 3.1 Localisation Methods ......................................... ................ .. .. ... .................. ............................. 54 3.3.2 VG+ Octree Localisation ... ....................................................................... .. ........................... 56 3 .4 CELL REFINEMENT ........................................................................................................................... 60 3. 4.1 Refinement Criteria for 'Clean' Geometries ............................ ................................................ 60 3.4.2 Internal and External Notions ........................... .... ........... ....... ............................................... .. 62 3. 4. 3 Refinement Criteria for 'Dirty' Geometries ..... ..... .... .... ..................................... ...................... 64 lV 3.4.4 Summary ofthe Refinement Criteria .............................................. ........ ...... .. ................... .... ... 68 3.5 FRONT GENERATION ......................................
Recommended publications
  • Balanced Trees Part One
    Balanced Trees Part One Balanced Trees ● Balanced search trees are among the most useful and versatile data structures. ● Many programming languages ship with a balanced tree library. ● C++: std::map / std::set ● Java: TreeMap / TreeSet ● Many advanced data structures are layered on top of balanced trees. ● We’ll see several later in the quarter! Where We're Going ● B-Trees (Today) ● A simple type of balanced tree developed for block storage. ● Red/Black Trees (Today/Thursday) ● The canonical balanced binary search tree. ● Augmented Search Trees (Thursday) ● Adding extra information to balanced trees to supercharge the data structure. Outline for Today ● BST Review ● Refresher on basic BST concepts and runtimes. ● Overview of Red/Black Trees ● What we're building toward. ● B-Trees and 2-3-4 Trees ● Simple balanced trees, in depth. ● Intuiting Red/Black Trees ● A much better feel for red/black trees. A Quick BST Review Binary Search Trees ● A binary search tree is a binary tree with 9 the following properties: 5 13 ● Each node in the BST stores a key, and 1 6 10 14 optionally, some auxiliary information. 3 7 11 15 ● The key of every node in a BST is strictly greater than all keys 2 4 8 12 to its left and strictly smaller than all keys to its right. Binary Search Trees ● The height of a binary search tree is the 9 length of the longest path from the root to a 5 13 leaf, measured in the number of edges. 1 6 10 14 ● A tree with one node has height 0.
    [Show full text]
  • Position Heaps for Cartesian-Tree Matching on Strings and Tries
    Position Heaps for Cartesian-tree Matching on Strings and Tries Akio Nishimoto1, Noriki Fujisato1, Yuto Nakashima1, and Shunsuke Inenaga1;2 1Department of Informatics, Kyushu University, Japan fnishimoto.akio, noriki.fujisato, yuto.nakashima, [email protected] 2PRESTO, Japan Science and Technology Agency, Japan Abstract The Cartesian-tree pattern matching is a recently introduced scheme of pattern matching that detects fragments in a sequential data stream which have a similar structure as a query pattern. Formally, Cartesian-tree pattern matching seeks all substrings S0 of the text string S such that the Cartesian tree of S0 and that of a query pattern P coincide. In this paper, we present a new indexing structure for this problem, called the Cartesian-tree Position Heap (CPH ). Let n be the length of the input text string S, m the length of a query pattern P , and σ the alphabet size. We show that the CPH of S, denoted CPH(S), supports pattern matching queries in O(m(σ + log(minfh; mg)) + occ) time with O(n) space, where h is the height of the CPH and occ is the number of pattern occurrences. We show how to build CPH(S) in O(n log σ) time with O(n) working space. Further, we extend the problem to the case where the text is a labeled tree (i.e. a trie). Given a trie T with N nodes, we show that the CPH of T , denoted CPH(T ), supports pattern matching queries on the trie in O(m(σ2 +log(minfh; mg))+occ) time with O(Nσ) space.
    [Show full text]
  • Lecture Notes of CSCI5610 Advanced Data Structures
    Lecture Notes of CSCI5610 Advanced Data Structures Yufei Tao Department of Computer Science and Engineering Chinese University of Hong Kong July 17, 2020 Contents 1 Course Overview and Computation Models 4 2 The Binary Search Tree and the 2-3 Tree 7 2.1 The binary search tree . .7 2.2 The 2-3 tree . .9 2.3 Remarks . 13 3 Structures for Intervals 15 3.1 The interval tree . 15 3.2 The segment tree . 17 3.3 Remarks . 18 4 Structures for Points 20 4.1 The kd-tree . 20 4.2 A bootstrapping lemma . 22 4.3 The priority search tree . 24 4.4 The range tree . 27 4.5 Another range tree with better query time . 29 4.6 Pointer-machine structures . 30 4.7 Remarks . 31 5 Logarithmic Method and Global Rebuilding 33 5.1 Amortized update cost . 33 5.2 Decomposable problems . 34 5.3 The logarithmic method . 34 5.4 Fully dynamic kd-trees with global rebuilding . 37 5.5 Remarks . 39 6 Weight Balancing 41 6.1 BB[α]-trees . 41 6.2 Insertion . 42 6.3 Deletion . 42 6.4 Amortized analysis . 42 6.5 Dynamization with weight balancing . 43 6.6 Remarks . 44 1 CONTENTS 2 7 Partial Persistence 47 7.1 The potential method . 47 7.2 Partially persistent BST . 48 7.3 General pointer-machine structures . 52 7.4 Remarks . 52 8 Dynamic Perfect Hashing 54 8.1 Two random graph results . 54 8.2 Cuckoo hashing . 55 8.3 Analysis . 58 8.4 Remarks . 59 9 Binomial and Fibonacci Heaps 61 9.1 The binomial heap .
    [Show full text]
  • CMSC 420: Lecture 7 Randomized Search Structures: Treaps and Skip Lists
    CMSC 420 Dave Mount CMSC 420: Lecture 7 Randomized Search Structures: Treaps and Skip Lists Randomized Data Structures: A common design techlque in the field of algorithm design in- volves the notion of using randomization. A randomized algorithm employs a pseudo-random number generator to inform some of its decisions. Randomization has proved to be a re- markably useful technique, and randomized algorithms are often the fastest and simplest algorithms for a given application. This may seem perplexing at first. Shouldn't an intelligent, clever algorithm designer be able to make better decisions than a simple random number generator? The issue is that a deterministic decision-making process may be susceptible to systematic biases, which in turn can result in unbalanced data structures. Randomness creates a layer of \independence," which can alleviate these systematic biases. In this lecture, we will consider two famous randomized data structures, which were invented at nearly the same time. The first is a randomized version of a binary tree, called a treap. This data structure's name is a portmanteau (combination) of \tree" and \heap." It was developed by Raimund Seidel and Cecilia Aragon in 1989. (Remarkably, this 1-dimensional data structure is closely related to two 2-dimensional data structures, the Cartesian tree by Jean Vuillemin and the priority search tree of Edward McCreight, both discovered in 1980.) The other data structure is the skip list, which is a randomized version of a linked list where links can point to entries that are separated by a significant distance. This was invented by Bill Pugh (a professor at UMD!).
    [Show full text]
  • Multidimensional Point Data 1
    CMSC 420 Summer 1998 Samet Multidimensional Point Data 1 Hanan Samet Computer Science Department University of Maryland College Park, Maryland 20742 September 10, 1998 1 Copyright c 1998 by Hanan Samet. These notes may not be reproducedby any means (mechanical, electronic, or any other) without the express written permission of Hanan Samet. Multidimensional Point Data The representation of multidimensional point data is a central issue in database design, as well as applications in many other ®elds that includecomputer graphics, computer vision, computationalgeometry, image process- ing, geographic information systems (GIS), pattern recognition, VLSI design, and others. These points can represent locations and objects in space as well as more general records. As an example of a general record, consider an employee record which has attributes corresponding to the employee's name, address, sex, age, height, weight, and social security number. Such records arise in database management systems and can be treated as points in, for this example, a seven-dimensional space (i.e., there is one dimension for each attribute or key2 ) albeit the different dimensions have different type units (i.e., name and address are strings of charac- ters, sex is binary; while age, height, weight, and social security number are numbers). Formally speaking, a database is a collection of records, termed a ®le. There is one record per data point, and each record contains several attributes or keys. In order to facilitate retrieval of a record based on some of its attribute values, we assume the existence of an ordering for the range of values of each of these attributes.
    [Show full text]
  • Cartesian Tree Matching and Indexing
    Cartesian Tree Matching and Indexing Sung Gwan Park Seoul National University, Korea [email protected] Amihood Amir Bar-Ilan University, Israel [email protected] Gad M. Landau University of Haifa, Israel and New York University, USA [email protected] Kunsoo Park1 Seoul National University, Korea [email protected] Abstract We introduce a new metric of match, called Cartesian tree matching, which means that two strings match if they have the same Cartesian trees. Based on Cartesian tree matching, we define single pattern matching for a text of length n and a pattern of length m, and multiple pattern matching for a text of length n and k patterns of total length m. We present an O(n + m) time algorithm for single pattern matching, and an O((n + m) log k) deterministic time or O(n + m) randomized time algorithm for multiple pattern matching. We also define an index data structure called Cartesian suffix tree, and present an O(n) randomized time algorithm to build the Cartesian suffix tree. Our efficient algorithms for Cartesian tree matching use a representation of the Cartesian tree, called the parent-distance representation. 2012 ACM Subject Classification Theory of computation → Pattern matching Keywords and phrases Cartesian tree matching, Pattern matching, Indexing, Parent-distance representation Digital Object Identifier 10.4230/LIPIcs.CPM.2019.13 1 Introduction String matching is one of fundamental problems in computer science, and it can be applied to many practical problems. In many applications string matching has variants derived from exact matching (which can be collectively called generalized matching), such as order- preserving matching [19, 20, 22], parameterized matching [4, 7, 8], jumbled matching [9], overlap matching [3], pattern matching with swaps [2], and so on.
    [Show full text]
  • Exercise Sheet 5. Data Structures Sose 2020 L´Aszl´Okozma, Katharina Klost Due 12:00, May 29Th, 2020
    Exercise sheet 5. Data structures SoSe 2020 L´aszl´oKozma, Katharina Klost Due 12:00, May 29th, 2020 Exercise 1 Cartesian trees 4 × 2 + 2 × 3 Points Recall the Cartesian tree (treap) built from an array A, using indices 1; : : : ; n as search keys, and the corresponding array entries A[1];:::;A[n] as min-heap priori- ties. (a) Which Cartesian tree corresponds to the array [1; 2; : : : ; n]? What about the array [1; 3; 5;:::; 2n + 1; 2; 4; 6;:::; 2n]? (b) Give an array of size 15 whose Cartesian tree is a complete balanced tree. (c) A left-to-right minimum of an array is an entry that is smaller than all pre- ceding entries. Show how to find the left-to-right minima of an array A in the Cartesian tree of A. (d) Let rmq(i; j) denote the index of the smallest entry in A[i : : : j], and let lca(i; j) denote the lowest common ancestor of i and j in the Cartesian tree of A. Show that lca(i; j) = rmq(i; j). (We sketched this in the lecture.) (e) Suppose now that the array A contains a permutation of the integers 1; : : : ; n chosen uniformly at random from the set of all permutations. Let T be the Cartesian tree of A. Given i and j, what is the probability (depending on i and j) that node (i; A[i]) is the ancestor of node (j; A[j])? Hint: x is the ancestor of y if and only if x = lca(x; y).
    [Show full text]
  • On Cartesian Trees and Range Minimum Queries
    On Cartesian trees and range minimum queries The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Demaine, Erik, Gad Landau, and Oren Weimann. “On Cartesian Trees and Range Minimum Queries.” Automata, Languages and Programming. Springer Berlin / Heidelberg, 2009. 341-353-353. As Published http://dx.doi.org/10.1007/978-3-642-02927-1_29 Publisher Springer Berlin / Heidelberg Version Author's final manuscript Citable link http://hdl.handle.net/1721.1/61963 Terms of Use Creative Commons Attribution-Noncommercial-Share Alike 3.0 Detailed Terms http://creativecommons.org/licenses/by-nc-sa/3.0/ On Cartesian Trees and Range Minimum Queries Erik D. Demaine?1 Gad M. Landau??2;3 and Oren Weimann∗1 1 MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA. fedemaine,[email protected] 2 Department of Computer Science, University of Haifa, Haifa, Israeal 3 Department of Computer and Information Science, Polytechnic Institute of NYU. [email protected] Abstract. We present new results on Cartesian trees with applications in range minimum queries and bottleneck edge queries. We introduce a cache- oblivious Cartesian tree for solving the range minimum query problem, a Cartesian tree of a tree for the bottleneck edge query problem on trees and undirected graphs, and a proof that no Cartesian tree exists for the two-dimensional version of the range minimum query problem. 1 Introduction In the Range Minimum Query (RMQ) problem, we wish to preprocess an array A of n numbers for subsequent queries asking for minfA[i];:::;A[j]g.
    [Show full text]
  • Cartesian Trees
    Range Minimum Queries Part Two Recap from Last Time The RMQ Problem ● The Range Minimum Query (RMQ) problem is the following: Given a fixed array A and two indices i ≤ j, what is the smallest element out of A[i], A[i + 1], …, A[j – 1], A[j]? 31 41 59 26 53 58 97 93 Some Notation ● We'll say that an RMQ data structure has time complexity ⟨p(n), q(n)⟩ if ● preprocessing takes time at most p(n) and ● queries take time at most q(n). ● Last time, we saw structures with the following runtimes: ● ⟨O(n2), O(1)⟩ (full preprocessing) ● ⟨O(n log n), O(1)⟩ (sparse table) ● ⟨O(n log log n), O(1)⟩ (hybrid approach) ● ⟨O(n), O(n1/2)⟩ (blocking) ● ⟨O(n), O(log n)⟩ (hybrid approach) ● ⟨O(n), O(log log n)⟩ (hybrid approach) The Framework ● Split the input into blocks of size b. ● Form an array of the block minima. ● Construct a “summary” RMQ structure over the block minima. ● Construct “block” RMQ structures for each block. ● Aggregate the results together. Summary RMQ 31 26 23 62 27 31 41 59 26 53 58 97 93 23 84 62 64 33 83 27 Block-Level Block-Level Block-Level Block-Level Block-Level RMQ RMQ RMQ RMQ RMQ The Framework ● Split the input into blocks of size b. ● Form an array of the block minima. ● Construct a “summary” RMQ structure over the block minima. ● Construct “block” RMQ structures for each block. ● Aggregate the results together. Summary RMQ 31 26 23 62 27 31 41 59 26 53 58 97 93 23 84 62 64 33 83 27 Block-Level Block-Level Block-Level Block-Level Block-Level RMQ RMQ RMQ RMQ RMQ The Framework ● Split the input into blocks of size b.
    [Show full text]
  • Challenge 2 Cartesian Trees
    Challenge 2 Cartesian Trees VerifyThis at ETAPS 2019 Organizers: Claire Dross, Carlo A. Furia, Marieke Huisman, Rosemary Monahan, Peter Müller 6–7 April 2019, Prague, Czech Republic How to submit solutions: send an email to [email protected] with your solution in attachment. Remember to clearly identify you, stating your group’s name and its members. This challenge1 contains two parts. We do not expect participants to com- plete the whole challenge in an hour and a half; you can chose the part you like best/which fits your language of choice best. The first part of the challenge is the easiest, it may be interesting to have a look at it first. Note that you do not need to actually implement and verify the algorithm of the first part to try the second. 1 Part A: All Nearest Smaller Values For each position in a sequence of values, we define the nearest smaller value to the left, or left neighbor, as the last position among the previous positions that contains a smaller value. More precisely, for each position x in an input sequence s, we define the left neighbor of x in s to be a position y such that: • y < x, • the element stored at position y in s, written s[y] is smaller than the ele- ment stored at position x in s, • there are no other values smaller than s[x] between y and x. There are positions which do not have a left neighbor (the first element, or the smallest element in the sequence for example).
    [Show full text]
  • MIT 6.851 Advanced Data Structures Prof
    MIT 6.851 Advanced Data Structures Prof. Erik Demaine Spring '12 Scribe Notes Collection TA: Tom Morgan, Justin Zhang Editing: Justin Zhang Contents 1 1. Temporal data structure 1 4 Scribers: Oscar Moll (2012), Aston Motes (2007), Kevin Wang (2007) 1.1 Overview . 4 1.2 Model and definitions . 4 1.3 Partial persistence . 6 1.4 Full persistence . 9 1.5 Confluent Persistence . 12 1.6 Functional persistence . 13 2 2. Temporal data structure 2 14 Scribers: Erek Speed (2012), Victor Jakubiuk (2012), Aston Motes (2007), Kevin Wang (2007) 2.1 Overview . 14 2.2 Retroactivity . 14 3 3. Geometric data structure 1 24 Scribers: Brian Hamrick (2012), Ben Lerner (2012), Keshav Puranmalka (2012) 3.1 Overview . 24 3.2 Planar Point Location . 24 3.3 Orthogonal range searching . 27 3.4 Fractional Cascading . 33 4 4. Geometric data structure 2 35 2 Scribers: Brandon Tran (2012), Nathan Pinsker (2012), Ishaan Chugh (2012), David Stein (2010), Jacob Steinhardt (2010) 4.1 Overview- Geometry II . 35 4.2 3D Orthogonal Range Search in O(lg n) Query Time . 35 4.3 Kinetic Data Structures . 38 5 5. Dynamic optimality 1 42 Scribers: Brian Basham (2012), Travis Hance (2012), Jayson Lynch (2012) 5.1 Overview . 42 5.2 Binary Search Trees . 42 5.3 Splay Trees . 45 5.4 Geometric View . 46 6 6. Dynamic optimality 2 50 Scribers: Aakanksha Sarda (2012), David Field (2012), Leonardo Urbina (2012), Prasant Gopal (2010), Hui Tang (2007), Mike Ebersol (2005) 6.1 Overview . 50 6.2 Independent Rectangle Bounds . 50 6.3 Lower Bounds .
    [Show full text]
  • CMSC 420 Data Structures1
    CMSC 420 Data Structures1 David M. Mount Department of Computer Science University of Maryland Fall 2019 1 Copyright, David M. Mount, 2019, Dept. of Computer Science, University of Maryland, College Park, MD, 20742. These lecture notes were prepared by David Mount for the course CMSC 420, Data Structures, at the University of Maryland. Permission to use, copy, modify, and distribute these notes for educational purposes and without fee is hereby granted, provided that this copyright notice appear in all copies. Lecture Notes 1 CMSC 420 Lecture 1: Course Introduction and Background Algorithms and Data Structures: The study of data structures and the algorithms that ma- nipulate them is among the most fundamental topics in computer science. Most of what computer systems spend their time doing is storing, accessing, and manipulating data in one form or another. Some examples from computer science include: Information Retrieval: List the 10 most informative Web pages on the subject of \how to treat high blood pressure?" Identify possible suspects of a crime based on fingerprints or DNA evidence. Find movies that a Netflix subscriber may like based on the movies that this person has already viewed. Find images on the Internet containing both kangaroos and horses. Geographic Information Systems: How many people in the USA live within 25 miles of the Mississippi River? List the 10 movie theaters that are closest to my current location. If sea levels rise 10 meters, what fraction of Florida will be under water? Compilers: You need to store a set of variable names along with their associated types. Given an assignment between two variables we need to look them up in a symbol table, determine their types, and determine whether it is possible to cast from one type to the other).
    [Show full text]