Course Description
Total Page：16
File Type：pdf, Size：1020Kb
Load more
Recommended publications
-
Curriculum for Second Year of Computer Engineering (2019 Course) (With Effect from 2020-21)
Faculty of Science and Technology Savitribai Phule Pune University Maharashtra, India Curriculum for Second Year of Computer Engineering (2019 Course) (With effect from 2020-21) www.unipune.ac.in Savitribai Phule Pune University Savitribai Phule Pune University Bachelor of Computer Engineering Program Outcomes (PO) Learners are expected to know and be able to– PO1 Engineering Apply the knowledge of mathematics, science, Engineering fundamentals, knowledge and an Engineering specialization to the solution of complex Engineering problems PO2 Problem analysis Identify, formulate, review research literature, and analyze complex Engineering problems reaching substantiated conclusions using first principles of mathematics natural sciences, and Engineering sciences PO3 Design / Development Design solutions for complex Engineering problems and design system of Solutions components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and Environmental considerations PO4 Conduct Use research-based knowledge and research methods including design of Investigations of experiments, analysis and interpretation of data, and synthesis of the Complex Problems information to provide valid conclusions. PO5 Modern Tool Usage Create, select, and apply appropriate techniques, resources, and modern Engineering and IT tools including prediction and modeling to complex Engineering activities with an understanding of the limitations PO6 The Engineer and Apply reasoning informed by -
KP-Trie Algorithm for Update and Search Operations
The International Arab Journal of Information Technology, Vol. 13, No. 6, November 2016 722 KP-Trie Algorithm for Update and Search Operations Feras Hanandeh1, Izzat Alsmadi2, Mohammed Akour3, and Essam Al Daoud4 1Department of Computer Information Systems, Hashemite University, Jordan 2, 3Department of Computer Information Systems, Yarmouk University, Jordan 4Computer Science Department, Zarqa University, Jordan Abstract: Radix-Tree is a space optimized data structure that performs data compression by means of cluster nodes that share the same branch. Each node with only one child is merged with its child and is considered as space optimized. Nevertheless, it can’t be considered as speed optimized because the root is associated with the empty string. Moreover, values are not normally associated with every node; they are associated only with leaves and some inner nodes that correspond to keys of interest. Therefore, it takes time in moving bit by bit to reach the desired word. In this paper we propose the KP-Trie which is consider as speed and space optimized data structure that is resulted from both horizontal and vertical compression. Keywords: Trie, radix tree, data structure, branch factor, indexing, tree structure, information retrieval. Received January 14, 2015; accepted March 23, 2015; Published online December 23, 2015 1. Introduction the exception of leaf nodes, nodes in the trie work merely as pointers to words. Data structures are a specialized format for efficient A trie, also called digital tree, is an ordered multi- organizing, retrieving, saving and storing data. It’s way tree data structure that is useful to store an efficient with large amount of data such as: Large data associative array where the keys are usually strings, bases. -
Lecture 04 Linear Structures Sort
Algorithmics (6EAP) MTAT.03.238 Linear structures, sorting, searching, etc Jaak Vilo 2018 Fall Jaak Vilo 1 Big-Oh notation classes Class Informal Intuition Analogy f(n) ∈ ο ( g(n) ) f is dominated by g Strictly below < f(n) ∈ O( g(n) ) Bounded from above Upper bound ≤ f(n) ∈ Θ( g(n) ) Bounded from “equal to” = above and below f(n) ∈ Ω( g(n) ) Bounded from below Lower bound ≥ f(n) ∈ ω( g(n) ) f dominates g Strictly above > Conclusions • Algorithm complexity deals with the behavior in the long-term – worst case -- typical – average case -- quite hard – best case -- bogus, cheating • In practice, long-term sometimes not necessary – E.g. for sorting 20 elements, you dont need fancy algorithms… Linear, sequential, ordered, list … Memory, disk, tape etc – is an ordered sequentially addressed media. Physical ordered list ~ array • Memory /address/ – Garbage collection • Files (character/byte list/lines in text file,…) • Disk – Disk fragmentation Linear data structures: Arrays • Array • Hashed array tree • Bidirectional map • Heightmap • Bit array • Lookup table • Bit field • Matrix • Bitboard • Parallel array • Bitmap • Sorted array • Circular buffer • Sparse array • Control table • Sparse matrix • Image • Iliffe vector • Dynamic array • Variable-length array • Gap buffer Linear data structures: Lists • Doubly linked list • Array list • Xor linked list • Linked list • Zipper • Self-organizing list • Doubly connected edge • Skip list list • Unrolled linked list • Difference list • VList Lists: Array 0 1 size MAX_SIZE-1 3 6 7 5 2 L = int[MAX_SIZE] -
Guided Local Search
GUIDED LOCAL SEARCH Christos Voudouris, Edward P. K. Tsang and Abdullah Alsheddy Abstract Combinatorial explosion problem is a well known phenomenon that pre- vents complete algorithms from solving many real-life combinatorial optimization problems. In many situations, heuristic search methods are needed. This chapter de- scribes the principles of Guided Local Search (GLS) and Fast Local Search (FLS) and surveys their applications. GLS is a penalty-based meta-heuristic algorithm that sits on top of other local search algorithms, with the aim to improve their efficiency and robustness. FLS is a way of reducing the size of the neighbourhood to improve the efficiency of local search. The chapter also provides guidance for implement- ing and using GLS and FLS. Four problems, representative of general application categories, are examined with detailed information provided on how to build a GLS- based method in each case. Key words: Heuristic Search, Meta-Heuristics, Penalty-based Methods, Guided Local Search, Tabu Search, Constraint Satisfaction. 1 INTRODUCTION Many practical problems are NP-hard in nature, which means complete, construc- tive search is unlikely to satisfy our computational demand. For example, suppose Christos Voudouris Group Chief Technology Office, BT plc, Orion Building, mlb1/pp12, Marthlesham Heath, Ipswich, IP5 3RE, United Kingdom, e-mail: [email protected] Edward P. K. Tsang Department of Computer Science, Univeristy of Essex, Wivenhoe Park, Colchester, CO4 3SQ, United Kingdom, e-mail: [email protected] Abdullah Alsheddy Department of Computer Science, Univeristy of Essex, Wivenhoe Park, Colchester, CO4 3SQ, United Kingdom, e-mail: [email protected] 1 2 Christos Voudouris, Edward P. -
(Stochastic) Local Search Algorithms
DM841 DISCRETE OPTIMIZATION Part 2 – Heuristics (Stochastic) Local Search Algorithms Marco Chiarandini Department of Mathematics & Computer Science University of Southern Denmark Local Search Algorithms Basic Algorithms Outline Local Search Revisited 1. Local Search Algorithms 2. Basic Algorithms 3. Local Search Revisited Components 2 Local Search Algorithms Basic Algorithms Outline Local Search Revisited 1. Local Search Algorithms 2. Basic Algorithms 3. Local Search Revisited Components 3 Local Search Algorithms Basic Algorithms Local Search Algorithms Local Search Revisited Given a (combinatorial) optimization problem Π and one of its instances π: 1. search space S(π) I specified by the definition of (finite domain, integer) variables and their values handling implicit constraints I all together they determine the representation of candidate solutions I common solution representations are discrete structures such as: sequences, permutations, partitions, graphs (e.g., for SAT: array, sequence of truth assignments to propositional variables) Note: solution set S 0(π) ⊆ S(π) (e.g., for SAT: models of given formula) 4 Local Search Algorithms Basic Algorithms Local Search Algorithms (cntd) Local Search Revisited 2. evaluation function fπ : S(π) ! R I it handles the soft constraints and the objective function (e.g., for SAT: number of false clauses) S(π) 3. neighborhood function, Nπ : S ! 2 I defines for each solution s 2 S(π) a set of solutions N(s) ⊆ S(π) that are in some sense close to s. (e.g., for SAT: neighboring variable assignments differ in the truth value of exactly one variable) 5 Local Search Algorithms Basic Algorithms Local Search Algorithms (cntd) Local Search Revisited Further components [according to [HS]] 4. -
Assignment of Master's Thesis
CZECH TECHNICAL UNIVERSITY IN PRAGUE FACULTY OF INFORMATION TECHNOLOGY ASSIGNMENT OF MASTER’S THESIS Title: Approximate Pattern Matching In Sparse Multidimensional Arrays Using Machine Learning Based Methods Student: Bc. Anna Kučerová Supervisor: Ing. Luboš Krčál Study Programme: Informatics Study Branch: Knowledge Engineering Department: Department of Theoretical Computer Science Validity: Until the end of winter semester 2018/19 Instructions Sparse multidimensional arrays are a common data structure for effective storage, analysis, and visualization of scientific datasets. Approximate pattern matching and processing is essential in many scientific domains. Previous algorithms focused on deterministic filtering and aggregate matching using synopsis style indexing. However, little work has been done on application of heuristic based machine learning methods for these approximate array pattern matching tasks. Research current methods for multidimensional array pattern matching, discovery, and processing. Propose a method for array pattern matching and processing tasks utilizing machine learning methods, such as kernels, clustering, or PSO in conjunction with inverted indexing. Implement the proposed method and demonstrate its efficiency on both artificial and real world datasets. Compare the algorithm with deterministic solutions in terms of time and memory complexities and pattern occurrence miss rates. References Will be provided by the supervisor. doc. Ing. Jan Janoušek, Ph.D. prof. Ing. Pavel Tvrdík, CSc. Head of Department Dean Prague February 28, 2017 Czech Technical University in Prague Faculty of Information Technology Department of Knowledge Engineering Master’s thesis Approximate Pattern Matching In Sparse Multidimensional Arrays Using Machine Learning Based Methods Bc. Anna Kuˇcerov´a Supervisor: Ing. LuboˇsKrˇc´al 9th May 2017 Acknowledgements Main credit goes to my supervisor Ing. -
Balanced Binary Search Trees 1 Introduction 2 BST Analysis
csce750 — Analysis of Algorithms Fall 2020 — Lecture Notes: Balanced Binary Search Trees This document contains slides from the lecture, formatted to be suitable for printing or individ- ual reading, and with some supplemental explanations added. It is intended as a supplement to, rather than a replacement for, the lectures themselves — you should not expect the notes to be self-contained or complete on their own. 1 Introduction CLRS 12, 13 A binary search tree is a data structure that supports these operations: • INSERT(k) • SEARCH(k) • DELETE(k) Basic idea: Store one key at each node. • All keys in the left subtree of n are less than the key stored at n. • All keys in the right subtree of n are greater than the key stored at n. Search and insert are trivial. Delete is slightly trickier, but not too bad. You may notice that these operations are very similar to the opera- tions available for hash tables. However, data structures like BSTs remain important because they can be extended to efficiently sup- port other useful operations like iterating over the elements in order, and finding the largest and smallest elements. These things cannot be done efficiently in hash tables. 2 BST Analysis Each operation can be done in time O(h) on a BST of height h. Worst case: Θ(n) Aside: Does randomization help? • Answer: Sort of. If we know all of the keys at the start, and insert them in a random order, in which each of the n! permutations is equally likely, then the expected tree height is O(lg n). -
Exploring High-Order Neighborhoods by Pattern Mining and Injection
PILS: Exploring high-order neighborhoods by pattern mining and injection Florian Arnolda, ´Italo Santanab, Kenneth S¨orensena, Thibaut Vidalb∗ a University of Antwerp, Department of Engineering Management, Belgium fflorian.arnold,[email protected] bDepartamento de Inform´atica,Pontif´ıciaUniversidade Cat´olicado Rio de Janeiro (PUC-Rio) fisantana,[email protected] Abstract. We introduce pattern injection local search (PILS), an optimization strategy that uses pattern mining to explore high-order local-search neighborhoods, and illustrate its application on the vehicle routing problem. PILS operates by storing a limited number of frequent patterns from elite solutions. During the local search, each pattern is used to define one move in which 1) incompatible edges are disconnected, 2) the edges defined by the pattern are reconnected, and 3) the remaining solution fragments are optimally reconnected. Each such move is accepted only in case of solution improvement. As visible in our experiments, this strategy results in a new paradigm of local search, which complements and enhances classical search approaches in a controllable amount of computational time. We demonstrate that PILS identifies useful high-order moves (e.g., 9-opt and 10-opt) which would otherwise not be found by enumeration, and that it significantly improves the performance of state-of-the-art population-based and neighborhood-centered metaheuristics. Keywords. Local search, Pattern mining, Combinatorial optimization, Vehicle routing problem ∗ Corresponding author 1. Introduction Since the \no free lunch" theorem [46], it is known that no method can | on average | perform arXiv:1912.11462v1 [cs.AI] 24 Dec 2019 better than any other method on all possible problems. -
On the Design of (Meta)Heuristics
On the design of (meta)heuristics Tony Wauters CODeS Research group, KU Leuven, Belgium There are two types of people • Those who use heuristics • And those who don’t ☺ 2 Tony Wauters, Department of Computer Science, CODeS research group Heuristics Origin: Ancient Greek: εὑρίσκω, "find" or "discover" Oxford Dictionary: Proceeding to a solution by trial and error or by rules that are only loosely defined. Tony Wauters, Department of Computer Science, CODeS research group 3 Properties of heuristics + Fast* + Scalable* + High quality solutions* + Solve any type of problem (complex constraints, non-linear, …) - Problem specific (but usually transferable to other problems) - Cannot guarantee optimallity * if well designed 4 Tony Wauters, Department of Computer Science, CODeS research group Heuristic types • Constructive heuristics • Metaheuristics • Hybrid heuristics • Matheuristics: hybrid of Metaheuristics and Mathematical Programming • Other hybrids: Machine Learning, Constraint Programming,… • Hyperheuristics 5 Tony Wauters, Department of Computer Science, CODeS research group Constructive heuristics • Incrementally construct a solution from scratch. • Easy to understand and implement • Usually very fast • Reasonably good solutions 6 Tony Wauters, Department of Computer Science, CODeS research group Metaheuristics A Metaheuristic is a high-level problem independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms. Instead of reinventing the wheel when developing a heuristic -
A Novel LP-Based Local Search Technique – Fast and Quite Good –
A Novel LP-based Local Search Technique – Fast and Quite Good – Vom Fachbereich Informatik der Technische Universit¨at Darmstadt zur Erlangung des akademischen Grades eines Dr. rer. nat. genehmigte Dissertation von Herrn Alaubek Avdil (M.Sc.) aus der Ulaanbaatar, Mongolei Referent: Professor Dr. Karsten Weihe Korreferent: Professor Dr. Matthias M¨uller-Hannemann Tag der Einreichung: 04. Juni 2009 Tag der m¨undlichen Pr¨ufung: 17. Juli 2009 Darmstadt 2009 D 17 ii Acknowledgments I want to express my deep and sincere gratitude to my supervisor Prof. Karsten Weihe for enabling my doctoral study, inspiring with scientific work and motivating and providing me pleasant research and work atmosphere. Without his trust and encouragement my research would not be done and this theses would not exist. I am extremely grateful to Prof. Matthias M¨uller-Hannemann for his invaluable support and advice on my research, excellent and critical review to improve the presentation of my work, and for his friendship, and making it possible for my young family be close with me during my doctoral study in Germany. I would like to express my warm and sincere thanks to the colleagues and members of Algorithmics Group at the Department of Computer Science of Technische Universit¨at Darmstadt for their support and valuable comments on my research. My special ackno- wledgment goes to Dr. Roland Martin for his unforgettable support and encouragement during my doctoral study, and his detailed review and constructive criticism during the writing process of my dissertation. I wish to extend my warmest thanks to all people, who have helped me with my work at the Department of Computer Science at Technische Universit¨at Darmstadt. -
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 . -
Simple Balanced Binary Search Trees
Simple Balanced Binary Search Trees Prabhakar Ragde Cheriton School of Computer Science University of Waterloo Waterloo, Ontario, Canada [email protected] Efficient implementations of sets and maps (dictionaries) are important in computer science, and bal- anced binary search trees are the basis of the best practical implementations. Pedagogically, however, they are often quite complicated, especially with respect to deletion. I present complete code (with justification and analysis not previously available in the literature) for a purely-functional implemen- tation based on AA trees, which is the simplest treatment of the subject of which I am aware. 1 Introduction Trees are a fundamental data structure, introduced early in most computer science curricula. They are easily motivated by the need to store data that is naturally tree-structured (family trees, structured doc- uments, arithmetic expressions, and programs). We also expose students to the idea that we can impose tree structure on data that is not naturally so, in order to implement efficient manipulation algorithms. The typical first example is the binary search tree. However, they are problematic. Naive insertion and deletion are easy to present in a first course using a functional language (usually the topic is delayed to a second course if an imperative language is used), but in the worst case, this im- plementation degenerates to a list, with linear running time for all operations. The solution is to balance the tree during operations, so that a tree with n nodes has height O(logn). There are many different ways of doing this, but most are too complicated to present this early in the curriculum, so they are usually deferred to a later course on algorithms and data structures, leaving a frustrating gap.