An English-Persian Dictionary of Algorithms and Data Structures
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
NI\SI\ I-J!'.~!.:}',-~)N, Vifm:Nu\ National Aeronautics and Space Administration Langley Research Center Hampton, Virginia 23665
https://ntrs.nasa.gov/search.jsp?R=19830020606 2020-03-21T01:58:58+00:00Z NASA Technical Memorandum 83216 NASA-TM-8321619830020606 DECISION-MAKING AND PROBLEM SOLVING METHODS IN AUTOMATION TECHNOLOGY W. W. HANKINS) J. E. PENNINGTON) AND L. K. BARKER MAY 1983 : "J' ,. (,lqir; '.I '. .' I_ \ i !- ,( ~, . L!\NGLEY F~I:~~~:r.. r~CH (TN! El~ L1B!(,!\,f(,(, NA~;I\ NI\SI\ I-J!'.~!.:}',-~)N, Vifm:nU\ National Aeronautics and Space Administration Langley Research Center Hampton, Virginia 23665 TABLE OF CONTENTS Page SUMMARY ••••••••••••••••••••••••••••••••• •• 1 INTRODUCTION •• .,... ••••••••••••••••••••••• •• 2 DETECTION AND RECOGNITION •••••••••••••••••••••••• •• 3 T,ypes of Sensors Computer Vision CONSIGHT-1 S,ystem SRI Vision Module Touch and Contact Sensors PLANNING AND SCHEDULING ••••••••••••••••••••••••• •• 7 Operations Research Methods Linear Systems Nonlinear S,ystems Network Models Queueing Theory D,ynamic Programming Simulation Artificial Intelligence (AI) Methods Nets of Action Hierarchies (NOAH) System Monitoring Resource Allocation Robotics LEARNING ••••• ••••••••••••••••••••••••••• •• 13 Expert Systems Learning Concept and Linguistic Constructions Through English Dialogue Natural Language Understanding Robot Learning The Computer as an Intelligent Partner Learning Control THEOREM PROVING ••••••••••••••••••••••••••••• •• 15 Logical Inference Disproof by Counterexample Proof by Contradiction Resolution Principle Heuristic Methods Nonstandard Techniques Mathematical Induction Analogies Question-Answering Systems Man-Machine Systems -
1 Lower Bound on Runtime of Comparison Sorts
CS 161 Lecture 7 { Sorting in Linear Time Jessica Su (some parts copied from CLRS) 1 Lower bound on runtime of comparison sorts So far we've looked at several sorting algorithms { insertion sort, merge sort, heapsort, and quicksort. Merge sort and heapsort run in worst-case O(n log n) time, and quicksort runs in expected O(n log n) time. One wonders if there's something special about O(n log n) that causes no sorting algorithm to surpass it. As a matter of fact, there is! We can prove that any comparison-based sorting algorithm must run in at least Ω(n log n) time. By comparison-based, I mean that the algorithm makes all its decisions based on how the elements of the input array compare to each other, and not on their actual values. One example of a comparison-based sorting algorithm is insertion sort. Recall that insertion sort builds a sorted array by looking at the next element and comparing it with each of the elements in the sorted array in order to determine its proper position. Another example is merge sort. When we merge two arrays, we compare the first elements of each array and place the smaller of the two in the sorted array, and then we make other comparisons to populate the rest of the array. In fact, all of the sorting algorithms we've seen so far are comparison sorts. But today we will cover counting sort, which relies on the values of elements in the array, and does not base all its decisions on comparisons. -
AI-KI-B Heuristic Search
AI-KI-B Heuristic Search Ute Schmid & Diedrich Wolter Practice: Johannes Rabold Cognitive Systems and Smart Environments Applied Computer Science, University of Bamberg last change: 3. Juni 2019, 10:05 Outline Introduction of heuristic search algorithms, based on foundations of problem spaces and search • Uniformed Systematic Search • Depth-First Search (DFS) • Breadth-First Search (BFS) • Complexity of Blocks-World • Cost-based Optimal Search • Uniform Cost Search • Cost Estimation (Heuristic Function) • Heuristic Search Algorithms • Hill Climbing (Depth-First, Greedy) • Branch and Bound Algorithms (BFS-based) • Best First Search • A* • Designing Heuristic Functions • Problem Types Cost and Cost Estimation • \Real" cost is known for each operator. • Accumulated cost g(n) for a leaf node n on a partially expanded path can be calculated. • For problems where each operator has the same cost or where no information about costs is available, all operator applications have equal cost values. For cost values of 1, accumulated costs g(n) are equal to path-length d. • Sometimes available: Heuristics for estimating the remaining costs to reach the final state. • h^(n): estimated costs to reach a goal state from node n • \bad" heuristics can misguide search! Cost and Cost Estimation cont. Evaluation Function: f^(n) = g(n) + h^(n) Cost and Cost Estimation cont. • \True costs" of an optimal path from an initial state s to a final state: f (s). • For a node n on this path, f can be decomposed in the already performed steps with cost g(n) and the yet to perform steps with true cost h(n). • h^(n) can be an estimation which is greater or smaller than the true costs. -
Quicksort Z Merge Sort Z T(N) = Θ(N Lg(N)) Z Not In-Place CSE 680 Z Selection Sort (From Homework) Prof
Sorting Review z Insertion Sort Introduction to Algorithms z T(n) = Θ(n2) z In-place Quicksort z Merge Sort z T(n) = Θ(n lg(n)) z Not in-place CSE 680 z Selection Sort (from homework) Prof. Roger Crawfis z T(n) = Θ(n2) z In-place Seems pretty good. z Heap Sort Can we do better? z T(n) = Θ(n lg(n)) z In-place Sorting Comppgarison Sorting z Assumptions z Given a set of n values, there can be n! 1. No knowledge of the keys or numbers we permutations of these values. are sorting on. z So if we look at the behavior of the 2. Each key supports a comparison interface sorting algorithm over all possible n! or operator. inputs we can determine the worst-case 3. Sorting entire records, as opposed to complexity of the algorithm. numbers,,p is an implementation detail. 4. Each key is unique (just for convenience). Comparison Sorting Decision Tree Decision Tree Model z Decision tree model ≤ 1:2 > z Full binary tree 2:3 1:3 z A full binary tree (sometimes proper binary tree or 2- ≤≤> > tree) is a tree in which every node other than the leaves has two c hildren <1,2,3> ≤ 1:3 >><2,1,3> ≤ 2:3 z Internal node represents a comparison. z Ignore control, movement, and all other operations, just <1,3,2> <3,1,2> <2,3,1> <3,2,1> see comparison z Each leaf represents one possible result (a permutation of the elements in sorted order). -
2011-2012 ABET Self-Study Questionnaire for Engineering
ABET SELF-STUDY REPOrt for Computer Engineering July 1, 2012 CONFIDENTIAL The information supplied in this Self-Study Report is for the confidential use of ABET and its authorized agents, and will not be disclosed without authorization of the institution concerned, except for summary data not identifiable to a specific institution. Table of Contents BACKGROUND INFORMATION ................................................................................... 5 A. Contact Information ............................................................................................. 5 B. Program History ................................................................................................... 6 C. Options ................................................................................................................. 7 D. Organizational Structure ...................................................................................... 8 E. Program Delivery Modes ..................................................................................... 8 F. Program Locations ............................................................................................... 9 G. Deficiencies, Weaknesses or Concerns from Previous Evaluation(s) ................. 9 H. Joint Accreditation ............................................................................................... 9 CRITERION 1. STUDENTS ........................................................................................... 10 A. Student Admissions .......................................................................................... -
Full Functional Verification of Linked Data Structures
Full Functional Verification of Linked Data Structures Karen Zee Viktor Kuncak Martin C. Rinard MIT CSAIL, Cambridge, MA, USA EPFL, I&C, Lausanne, Switzerland MIT CSAIL, Cambridge, MA, USA ∗ [email protected] viktor.kuncak@epfl.ch [email protected] Abstract 1. Introduction We present the first verification of full functional correctness for Linked data structures such as lists, trees, graphs, and hash tables a range of linked data structure implementations, including muta- are pervasive in modern software systems. But because of phenom- ble lists, trees, graphs, and hash tables. Specifically, we present the ena such as aliasing and indirection, it has been a challenge to de- use of the Jahob verification system to verify formal specifications, velop automated reasoning systems that are capable of proving im- written in classical higher-order logic, that completely capture the portant correctness properties of such data structures. desired behavior of the Java data structure implementations (with the exception of properties involving execution time and/or mem- 1.1 Background ory consumption). Given that the desired correctness properties in- In principle, standard specification and verification approaches clude intractable constructs such as quantifiers, transitive closure, should work for linked data structure implementations. But in prac- and lambda abstraction, it is a challenge to successfully prove the tice, many of the desired correctness properties involve logical generated verification conditions. constructs such transitive closure and -
An Evolutionary Approach for Sorting Algorithms
ORIENTAL JOURNAL OF ISSN: 0974-6471 COMPUTER SCIENCE & TECHNOLOGY December 2014, An International Open Free Access, Peer Reviewed Research Journal Vol. 7, No. (3): Published By: Oriental Scientific Publishing Co., India. Pgs. 369-376 www.computerscijournal.org Root to Fruit (2): An Evolutionary Approach for Sorting Algorithms PRAMOD KADAM AND Sachin KADAM BVDU, IMED, Pune, India. (Received: November 10, 2014; Accepted: December 20, 2014) ABstract This paper continues the earlier thought of evolutionary study of sorting problem and sorting algorithms (Root to Fruit (1): An Evolutionary Study of Sorting Problem) [1]and concluded with the chronological list of early pioneers of sorting problem or algorithms. Latter in the study graphical method has been used to present an evolution of sorting problem and sorting algorithm on the time line. Key words: Evolutionary study of sorting, History of sorting Early Sorting algorithms, list of inventors for sorting. IntroDUCTION name and their contribution may skipped from the study. Therefore readers have all the rights to In spite of plentiful literature and research extent this study with the valid proofs. Ultimately in sorting algorithmic domain there is mess our objective behind this research is very much found in documentation as far as credential clear, that to provide strength to the evolutionary concern2. Perhaps this problem found due to lack study of sorting algorithms and shift towards a good of coordination and unavailability of common knowledge base to preserve work of our forebear platform or knowledge base in the same domain. for upcoming generation. Otherwise coming Evolutionary study of sorting algorithm or sorting generation could receive hardly information about problem is foundation of futuristic knowledge sorting problems and syllabi may restrict with some base for sorting problem domain1. -
Improving the Performance of Bubble Sort Using a Modified Diminishing Increment Sorting
Scientific Research and Essay Vol. 4 (8), pp. 740-744, August, 2009 Available online at http://www.academicjournals.org/SRE ISSN 1992-2248 © 2009 Academic Journals Full Length Research Paper Improving the performance of bubble sort using a modified diminishing increment sorting Oyelami Olufemi Moses Department of Computer and Information Sciences, Covenant University, P. M. B. 1023, Ota, Ogun State, Nigeria. E- mail: [email protected] or [email protected]. Tel.: +234-8055344658. Accepted 17 February, 2009 Sorting involves rearranging information into either ascending or descending order. There are many sorting algorithms, among which is Bubble Sort. Bubble Sort is not known to be a very good sorting algorithm because it is beset with redundant comparisons. However, efforts have been made to improve the performance of the algorithm. With Bidirectional Bubble Sort, the average number of comparisons is slightly reduced and Batcher’s Sort similar to Shellsort also performs significantly better than Bidirectional Bubble Sort by carrying out comparisons in a novel way so that no propagation of exchanges is necessary. Bitonic Sort was also presented by Batcher and the strong point of this sorting procedure is that it is very suitable for a hard-wired implementation using a sorting network. This paper presents a meta algorithm called Oyelami’s Sort that combines the technique of Bidirectional Bubble Sort with a modified diminishing increment sorting. The results from the implementation of the algorithm compared with Batcher’s Odd-Even Sort and Batcher’s Bitonic Sort showed that the algorithm performed better than the two in the worst case scenario. The implication is that the algorithm is faster. -
Search Algorithms ◦ Breadth-First Search (BFS) ◦ Uniform-Cost Search ◦ Depth-First Search (DFS) ◦ Depth-Limited Search ◦ Iterative Deepening Best-First Search
9-04-2013 Uninformed (blind) search algorithms ◦ Breadth-First Search (BFS) ◦ Uniform-Cost Search ◦ Depth-First Search (DFS) ◦ Depth-Limited Search ◦ Iterative Deepening Best-First Search HW#1 due today HW#2 due Monday, 9/09/13, in class Continue reading Chapter 3 Formulate — Search — Execute 1. Goal formulation 2. Problem formulation 3. Search algorithm 4. Execution A problem is defined by four items: 1. initial state 2. actions or successor function 3. goal test (explicit or implicit) 4. path cost (∑ c(x,a,y) – sum of step costs) A solution is a sequence of actions leading from the initial state to a goal state Search algorithms have the following basic form: do until terminating condition is met if no more nodes to consider then return fail; select node; {choose a node (leaf) on the tree} if chosen node is a goal then return success; expand node; {generate successors & add to tree} Analysis ◦ b = branching factor ◦ d = depth ◦ m = maximum depth g(n) = the total cost of the path on the search tree from the root node to node n h(n) = the straight line distance from n to G n S A B C G h(n) 5.8 3 2.2 2 0 Uninformed search strategies use only the information available in the problem definition Breadth-first search ◦ Uniform-cost search Depth-first search ◦ Depth-limited search ◦ Iterative deepening search • Expand shallowest unexpanded node • Implementation: – fringe is a FIFO queue, i.e., new successors go at end idea: order the branches under each node so that the “most promising” ones are explored first g(n) is the total cost -
Space-Efficient Data Structures, Streams, and Algorithms
Andrej Brodnik Alejandro López-Ortiz Venkatesh Raman Alfredo Viola (Eds.) Festschrift Space-Efficient Data Structures, Streams, LNCS 8066 and Algorithms Papers in Honor of J. Ian Munro on the Occasion of His 66th Birthday 123 Lecture Notes in Computer Science 8066 Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen Editorial Board David Hutchison Lancaster University, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Alfred Kobsa University of California, Irvine, CA, USA Friedemann Mattern ETH Zurich, Switzerland John C. Mitchell Stanford University, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel Oscar Nierstrasz University of Bern, Switzerland C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen TU Dortmund University, Germany Madhu Sudan Microsoft Research, Cambridge, MA, USA Demetri Terzopoulos University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Gerhard Weikum Max Planck Institute for Informatics, Saarbruecken, Germany Andrej Brodnik Alejandro López-Ortiz Venkatesh Raman Alfredo Viola (Eds.) Space-Efficient Data Structures, Streams, and Algorithms Papers in Honor of J. Ian Munro on the Occasion of His 66th Birthday 13 Volume Editors Andrej Brodnik University of Ljubljana, Faculty of Computer and Information Science Ljubljana, Slovenia and University of Primorska, -
CSI33 Data Structures
Outline CSI33 Data Structures Department of Mathematics and Computer Science Bronx Community College December 2, 2015 CSI33 Data Structures Outline Outline 1 Chapter 13: Heaps, Balances Trees and Hash Tables Hash Tables CSI33 Data Structures Chapter 13: Heaps, Balances Trees and Hash TablesHash Tables Outline 1 Chapter 13: Heaps, Balances Trees and Hash Tables Hash Tables CSI33 Data Structures Chapter 13: Heaps, Balances Trees and Hash TablesHash Tables Python Dictionaries Various names are given to the abstract data type we know as a dictionary in Python: Hash (The languages Perl and Ruby use this terminology; implementation is a hash table). Map (Microsoft Foundation Classes C/C++ Library; because it maps keys to values). Dictionary (Python, Smalltalk; lets you "look up" a value for a key). Association List (LISP|everything in LISP is a list, but this type is implemented by hash table). Associative Array (This is the technical name for such a structure because it looks like an array whose 'indexes' in square brackets are key values). CSI33 Data Structures Chapter 13: Heaps, Balances Trees and Hash TablesHash Tables Python Dictionaries In Python, a dictionary associates a value (item of data) with a unique key (to identify and access the data). The implementation strategy is the same in any language that uses associative arrays. Representing the relation between key and value is a hash table. CSI33 Data Structures Chapter 13: Heaps, Balances Trees and Hash TablesHash Tables Python Dictionaries The Python implementation uses a hash table since it has the most efficient performance for insertion, deletion, and lookup operations. The running times of all these operations are better than any we have seen so far. -
A Complete Bibliography of Publications in Nordisk Tidskrift for Informationsbehandling, BIT, and BIT Numerical Mathematics
A Complete Bibliography of Publications in Nordisk Tidskrift for Informationsbehandling, BIT,andBIT Numerical Mathematics Nelson H. F. Beebe University of Utah Department of Mathematics, 110 LCB 155 S 1400 E RM 233 Salt Lake City, UT 84112-0090 USA Tel: +1 801 581 5254 FAX: +1 801 581 4148 E-mail: [email protected], [email protected], [email protected] (Internet) WWW URL: http://www.math.utah.edu/~beebe/ 09 June 2021 Version 3.54 Title word cross-reference [3105, 328, 469, 655, 896, 524, 873, 455, 779, 946, 2944, 297, 1752, 670, 2582, 1409, 1987, 915, 808, 761, 916, 2071, 2198, 1449, 780, 959, 1105, 1021, 497, 2589]. A(α) #24873 [1089]. [896, 2594, 333]. A∗ [2013]. A∗Ax = b [2369]. n A [1640, 566, 947, 1580, 1460]. A = a2 +1 − 0 n (3) [2450]. (A λB) [1414]. 0=1 [1242]. 1 [334]. α [824, 1580]. AN [1622]. A(#) [3439]. − 12 [3037, 2711]. 1 2 [1097]. 1:0 [3043]. 10 AX − XB = C [2195, 2006]. [838]. 11 [1311]. 2 AXD − BXC = E [1101]. B [2144, 1953, 2291, 2162, 3047, 886, 2551, 957, [2187, 1575, 1267, 1409, 1489, 1991, 1191, 2007, 2552, 1832, 949, 3024, 3219, 2194]. 2; 3 979, 1819, 1597, 1823, 1773]. β [824]. BN n − p − − [1490]. 2 1 [320]. 2 1 [100]. 2m 4 [1181]. BS [1773]. BSI [1446]. C0 [2906]. C1 [1105]. 3 [2119, 1953, 2531, 1351, 2551, 1292, [3202]. C2 [3108, 2422, 3000, 2036]. χ2 1793, 949, 1356, 2711, 2227, 570]. [30, 31]. Cln(θ); (n ≥ 2) [2929]. cos [228]. D 3; 000; 000; 000 [575, 637].