2. (a) Explain the strassen s matrix multiplication. (b) Write deletion algorithm, of Binary search tree. [8+8]


 Hector Cameron
 5 years ago
 Views:
Transcription
1 Code No: R Set No (a) Describe the performance analysis in detail. (b) Show that f 1 (n)+f 2 (n) = 0(max(g 1 (n), g 2 (n)) where f 1 (n) = 0(g 1 (n)) and f 2 (n) = 0(g 2 (n)). [8+8] 2. (a) Explain the strassen s matrix multiplication. (b) Write deletion algorithm, of Binary search tree. [8+8] 3. (a) Write Greedy algorithm to generate shortest path. (b) If p 1 /w 1 p 2 /w 2... p n /w n prove that knapsack generates an optimal solution to the given instance of the knapsack problem. [8+8] 4. (a) Explain matrix chain multiplication with an example. (b) Solve the following 0/1 Knapsack problem using dynamic programming P=(11,21,31,33), W=(2,11,22,15), C=40, n=4. [8+8] 5. (a) Explain the properties of strongly connected components. (b) Write a nonrecursive algorithm of Inorder traversal of a tree and also analyze its time complexity. [6+10] 6. (a) Write an algorithm of mcoloring problem. (b) Solve the 4queens problem using backtracking. [8+8] 7. (a) Describe problem state, solution state and answer state with an example. (b) Explain the general method of Branch and Bound. [8+8] 8. (a) Explain the classes of P and NP. (b) Write a nondeterministic Knapsack algorithm. [8+8]
2 Code No: R Set No (a) Define time complexity. Describe different notations used to represent there complexities. (b) Derive the function f(n)= 12n 2 +6n is 0(n 3 ) and w(n). [10+6] 2. (a) Suppose a binary tree has leaves l 1 l 2...l m at depths d 1, d 2...d m respectively prove that m 2 di 1 and determine when the equality is true. i=1 (b) Write and explain the control abstraction algorithm of divide and conquer. [8+8] 3. (a) Write a greedy algorithm to the Job sequencing with deadlines. (b) Prove that the edge with the smallest weight will be part of every minimum spanning tree. [8+8] 4. (a) Explain matrix chain multiplication with an example. (b) Solve the following 0/1 Knapsack problem using dynamic programming P=(11,21,31,33), W=(2,11,22,15), C=40, n=4. [8+8] 5. (a) Explain the BFS algorithm with an example. (b) The Preorder and Postorder sequences of a binary tree do not uniquely define the binary tree. Justify the answer. [8+8] 6. (a) Describe graph coloring problem and its time complexity. (b) Write an algorithm of 8queens problem using backtracking. [8+8] 7. (a) Write an algorithm to solve the Knapsack problem with the Branch and Bound (b) Differentiate between Dynamic Knapsack and Branch and Bound Knapsack problem. [10+6] 8. (a) Explain the classes of P and NP. (b) Write a nondeterministic Knapsack algorithm. [8+8]
3 Code No: R Set No (a) Consider a polynomial in n of the form f (n) = m a i n i. = a m n m + a m 1 n m a 2 n 2 + a 1 n + a 0 where a m > 0 i=0 then f (n) = Ω (n m ) (b) Differentiate between profilling and debugging. [10+6] 2. (a) Write and explain the control abstraction for Divide and conquer. (b) Suggest refinements to mergesort to make it inplace. [8+8] 3. State whether the following statements are true or false. Justify the answer. (a) If e is a minimum weight edge in a connected weighted graph, it must be among edges of at least one minimum spanning tree of the graph. (b) If e is a minimum weight edge in a connected weighted graph, it must be among edges of each minimum spanning tree of the graph. (c) If edge weights of a connected weighted graph are all distinct, the graph must have exactly are minimum spanning tree. (d) If edge weights of a connected weighted graph are not all distinct, the graph must have more than one minimum spanning tree. [16] 4. (a) In how many ways, the following chain of matrices may be multiplied? A X B X C X D [2X5] [5X3] [3X6] [6X4] Find the no. of multiplications required in each case. (b) Differentiate between Greedy method and Dynamic programming (c) Define merging and purging rules of O/1 Knapsack problem. [6+5+5] 5. (a) Explain game tree with an example. (b) Prove or disprove an undirected graph G=(V,E) is biconnected if and only if for each pair of distinct vertices u and v there are two distinct paths from u to v that have no vertex in common except u and v. [8+8] 6. (a) Draw the state space tree for m coloring when n=3 and m=3 (b) Write a recursive backtracking algorithm. [8+8] 7. (a) Explain the general method of Branch and Bound. 1 of 2
4 Code No: R Set No. 3 (b) Explain the principles of LIFO Branch and Bound. [8+8] 8. (a) Explain the classes of NPhard and NPcomplete. (b) Describe clique decision problem and write the algorithm for the same. [8+8] 2 of 2
5 Code No: R Set No (a) Develop a probabilistic algorithm to find the value of the integral x2 dx (b) Differentiate between priori analysis and posteriori analysis. [10+6] 2. (a) Write and explain the control abstraction for Divide and conquer. (b) Suggest refinements to mergesort to make it inplace. [8+8] 3. (a) What is spanning tree? Explain the prim s algorithm with an example. (b) Explain the terms Feasible solution, optimal solution and objective function. [10+6] 4. (a) Write a pseudocode for a linear time algorithm that generates the optimal Binary search tree from the root table. (b) Find the minimum no of operations required for the following chain matrix multiplication using dynamic programming. A(30,40) * B(40,5) * C(5,15) * D(15,6). [8+8] 5. Write an algorithm of Biconnected components and also analyze its time complexity. [16] 6. (a) Draw the state space tree for m coloring when n=3 and m=3 (b) Write a recursive backtracking algorithm. [8+8] 7. (a) Explain the method of reduction to solve TSP problem using Branch and Bound. (b) Explain the principles of FIFO Branch and Bound. [8+8] 8. (a) What is meant by Halting problem explain with an example. (b) Explain the classes of P and NP. [8+8]
Data Structure [Question Bank]
Unit I (Analysis of Algorithms) 1. What are algorithms and how they are useful? 2. Describe the factor on best algorithms depends on? 3. Differentiate: Correct & Incorrect Algorithms? 4. Write short note:
More informationExam study sheet for CS2711. List of topics
Exam study sheet for CS2711 Here is the list of topics you need to know for the final exam. For each data structure listed below, make sure you can do the following: 1. Give an example of this data structure
More informationNPCompleteness. CptS 223 Advanced Data Structures. Larry Holder School of Electrical Engineering and Computer Science Washington State University
NPCompleteness CptS 223 Advanced Data Structures Larry Holder School of Electrical Engineering and Computer Science Washington State University 1 Hard Graph Problems Hard means no known solutions with
More informationCMPSCI611: Approximating MAXCUT Lecture 20
CMPSCI611: Approximating MAXCUT Lecture 20 For the next two lectures we ll be seeing examples of approximation algorithms for interesting NPhard problems. Today we consider MAXCUT, which we proved to
More informationComputer Algorithms. NPComplete Problems. CISC 4080 Yanjun Li
Computer Algorithms NPComplete Problems NPcompleteness The quest for efficient algorithms is about finding clever ways to bypass the process of exhaustive search, using clues from the input in order
More informationWhy? A central concept in Computer Science. Algorithms are ubiquitous.
Analysis of Algorithms: A Brief Introduction Why? A central concept in Computer Science. Algorithms are ubiquitous. Using the Internet (sending email, transferring files, use of search engines, online
More informationConverting a Number from Decimal to Binary
Converting a Number from Decimal to Binary Convert nonnegative integer in decimal format (base 10) into equivalent binary number (base 2) Rightmost bit of x Remainder of x after division by two Recursive
More informationTutorial 8. NPComplete Problems
Tutorial 8 NPComplete Problems Decision Problem Statement of a decision problem Part 1: instance description defining the input Part 2: question stating the actual yesorno question A decision problem
More informationBinary Search Trees CMPSC 122
Binary Search Trees CMPSC 122 Note: This notes packet has significant overlap with the first set of trees notes I do in CMPSC 360, but goes into much greater depth on turning BSTs into pseudocode than
More informationMathematics for Algorithm and System Analysis
Mathematics for Algorithm and System Analysis for students of computer and computational science Edward A. Bender S. Gill Williamson c Edward A. Bender & S. Gill Williamson 2005. All rights reserved. Preface
More informationPage 1. CSCE 310J Data Structures & Algorithms. CSCE 310J Data Structures & Algorithms. P, NP, and NPComplete. PolynomialTime Algorithms
CSCE 310J Data Structures & Algorithms P, NP, and NPComplete Dr. Steve Goddard goddard@cse.unl.edu CSCE 310J Data Structures & Algorithms Giving credit where credit is due:» Most of the lecture notes
More informationOutline. NPcompleteness. When is a problem easy? When is a problem hard? Today. Euler Circuits
Outline NPcompleteness Examples of Easy vs. Hard problems Euler circuit vs. Hamiltonian circuit Shortest Path vs. Longest Path 2pairs sum vs. general Subset Sum Reducing one problem to another Clique
More informationKrishna Institute of Engineering & Technology, Ghaziabad Department of Computer Application MCA213 : DATA STRUCTURES USING C
Tutorial#1 Q 1: Explain the terms data, elementary item, entity, primary key, domain, attribute and information? Also give examples in support of your answer? Q 2: What is a Data Type? Differentiate
More information5 INTEGER LINEAR PROGRAMMING (ILP) E. Amaldi Fondamenti di R.O. Politecnico di Milano 1
5 INTEGER LINEAR PROGRAMMING (ILP) E. Amaldi Fondamenti di R.O. Politecnico di Milano 1 General Integer Linear Program: (ILP) min c T x Ax b x 0 integer Assumption: A, b integer The integrality condition
More informationGuessing Game: NPComplete?
Guessing Game: NPComplete? 1. LONGESTPATH: Given a graph G = (V, E), does there exists a simple path of length at least k edges? YES 2. SHORTESTPATH: Given a graph G = (V, E), does there exists a simple
More informationCSE 326, Data Structures. Sample Final Exam. Problem Max Points Score 1 14 (2x7) 2 18 (3x6) 3 4 4 7 5 9 6 16 7 8 8 4 9 8 10 4 Total 92.
Name: Email ID: CSE 326, Data Structures Section: Sample Final Exam Instructions: The exam is closed book, closed notes. Unless otherwise stated, N denotes the number of elements in the data structure
More informationDATA STRUCTURES USING C
DATA STRUCTURES USING C QUESTION BANK UNIT I 1. Define data. 2. Define Entity. 3. Define information. 4. Define Array. 5. Define data structure. 6. Give any two applications of data structures. 7. Give
More information1. Nondeterministically guess a solution (called a certificate) 2. Check whether the solution solves the problem (called verification)
Some N P problems Computer scientists have studied many N P problems, that is, problems that can be solved nondeterministically in polynomial time. Traditionally complexity question are studied as languages:
More informationProblem Set 7 Solutions
8 8 Introduction to Algorithms May 7, 2004 Massachusetts Institute of Technology 6.046J/18.410J Professors Erik Demaine and Shafi Goldwasser Handout 25 Problem Set 7 Solutions This problem set is due in
More information1) The postfix expression for the infix expression A+B*(C+D)/F+D*E is ABCD+*F/DE*++
Answer the following 1) The postfix expression for the infix expression A+B*(C+D)/F+D*E is ABCD+*F/DE*++ 2) Which data structure is needed to convert infix notations to postfix notations? Stack 3) The
More informationNear Optimal Solutions
Near Optimal Solutions Many important optimization problems are lacking efficient solutions. NPComplete problems unlikely to have polynomial time solutions. Good heuristics important for such problems.
More informationApproximation Algorithms
Approximation Algorithms or: How I Learned to Stop Worrying and Deal with NPCompleteness Ong Jit Sheng, Jonathan (A0073924B) March, 2012 Overview Key Results (I) General techniques: Greedy algorithms
More informationGRAPH THEORY LECTURE 4: TREES
GRAPH THEORY LECTURE 4: TREES Abstract. 3.1 presents some standard characterizations and properties of trees. 3.2 presents several different types of trees. 3.7 develops a counting method based on a bijection
More informationDiscrete Mathematics Problems
Discrete Mathematics Problems William F. Klostermeyer School of Computing University of North Florida Jacksonville, FL 32224 Email: wkloster@unf.edu Contents 0 Preface 3 1 Logic 5 1.1 Basics...............................
More informationFundamentals of algorithms
CHAPTER Fundamentals of algorithms 4 ChungYang (Ric) Huang National Taiwan University, Taipei, Taiwan ChaoYue Lai National Taiwan University, Taipei, Taiwan KwangTing (Tim) Cheng University of California,
More informationHome Page. Data Structures. Title Page. Page 1 of 24. Go Back. Full Screen. Close. Quit
Data Structures Page 1 of 24 A.1. Arrays (Vectors) nelement vector start address + ielementsize 0 +1 +2 +3 +4... +n1 start address continuous memory block static, if size is known at compile time dynamic,
More informationComplexity Theory. IE 661: Scheduling Theory Fall 2003 Satyaki Ghosh Dastidar
Complexity Theory IE 661: Scheduling Theory Fall 2003 Satyaki Ghosh Dastidar Outline Goals Computation of Problems Concepts and Definitions Complexity Classes and Problems Polynomial Time Reductions Examples
More informationA Fast Algorithm For Finding Hamilton Cycles
A Fast Algorithm For Finding Hamilton Cycles by Andrew Chalaturnyk A thesis presented to the University of Manitoba in partial fulfillment of the requirements for the degree of Masters of Science in Computer
More informationOn the Unique Games Conjecture
On the Unique Games Conjecture Antonios Angelakis National Technical University of Athens June 16, 2015 Antonios Angelakis (NTUA) Theory of Computation June 16, 2015 1 / 20 Overview 1 Introduction 2 Preliminary
More informationComplexity Classes P and NP
Complexity Classes P and NP MATH 3220 Supplemental Presentation by John Aleshunas The cure for boredom is curiosity. There is no cure for curiosity Dorothy Parker Computational Complexity Theory In computer
More informationA binary search tree is a binary tree with a special property called the BSTproperty, which is given as follows:
Chapter 12: Binary Search Trees A binary search tree is a binary tree with a special property called the BSTproperty, which is given as follows: For all nodes x and y, if y belongs to the left subtree
More informationCS5310 Algorithms 3 credit hours 2 hours lecture and 2 hours recitation every week
CS5310 Algorithms 3 credit hours 2 hours lecture and 2 hours recitation every week This course is a continuation of the study of data structures and algorithms, emphasizing methods useful in practice.
More informationLecture 7: NPComplete Problems
IAS/PCMI Summer Session 2000 Clay Mathematics Undergraduate Program Basic Course on Computational Complexity Lecture 7: NPComplete Problems David Mix Barrington and Alexis Maciel July 25, 2000 1. Circuit
More informationIE 680 Special Topics in Production Systems: Networks, Routing and Logistics*
IE 680 Special Topics in Production Systems: Networks, Routing and Logistics* Rakesh Nagi Department of Industrial Engineering University at Buffalo (SUNY) *Lecture notes from Network Flows by Ahuja, Magnanti
More informationTo My Parents Laxmi and Modaiah. To My Family Members. To My Friends. To IIT Bombay. To All Hard Workers
To My Parents Laxmi and Modaiah To My Family Members To My Friends To IIT Bombay To All Hard Workers Copyright 2010 by CareerMonk.com All rights reserved. Designed by Narasimha Karumanchi Printed in
More information5. A full binary tree with n leaves contains [A] n nodes. [B] log n 2 nodes. [C] 2n 1 nodes. [D] n 2 nodes.
1. The advantage of.. is that they solve the problem if sequential storage representation. But disadvantage in that is they are sequential lists. [A] Lists [B] Linked Lists [A] Trees [A] Queues 2. The
More informationSocial Media Mining. Graph Essentials
Graph Essentials Graph Basics Measures Graph and Essentials Metrics 2 2 Nodes and Edges A network is a graph nodes, actors, or vertices (plural of vertex) Connections, edges or ties Edge Node Measures
More informationCSC 373: Algorithm Design and Analysis Lecture 16
CSC 373: Algorithm Design and Analysis Lecture 16 Allan Borodin February 25, 2013 Some materials are from Stephen Cook s IIT talk and Keven Wayne s slides. 1 / 17 Announcements and Outline Announcements
More informationAlgorithms and data structures
Algorithms and data structures This course will examine various data structures for storing and accessing information together with relationships between the items being stored, and algorithms for efficiently
More informationChapter 11. 11.1 Load Balancing. Approximation Algorithms. Load Balancing. Load Balancing on 2 Machines. Load Balancing: Greedy Scheduling
Approximation Algorithms Chapter Approximation Algorithms Q. Suppose I need to solve an NPhard problem. What should I do? A. Theory says you're unlikely to find a polytime algorithm. Must sacrifice one
More informationWarshall s Algorithm: Transitive Closure
CS 0 Theory of Algorithms / CS 68 Algorithms in Bioinformaticsi Dynamic Programming Part II. Warshall s Algorithm: Transitive Closure Computes the transitive closure of a relation (Alternatively: all paths
More informationAny two nodes which are connected by an edge in a graph are called adjacent node.
. iscuss following. Graph graph G consist of a non empty set V called the set of nodes (points, vertices) of the graph, a set which is the set of edges and a mapping from the set of edges to a set of pairs
More information! Solve problem to optimality. ! Solve problem in polytime. ! Solve arbitrary instances of the problem. !approximation algorithm.
Approximation Algorithms Chapter Approximation Algorithms Q Suppose I need to solve an NPhard problem What should I do? A Theory says you're unlikely to find a polytime algorithm Must sacrifice one of
More informationNPcomplete? NPhard? Some Foundations of Complexity. Prof. Sven Hartmann Clausthal University of Technology Department of Informatics
NPcomplete? NPhard? Some Foundations of Complexity Prof. Sven Hartmann Clausthal University of Technology Department of Informatics Tractability of Problems Some problems are undecidable: no computer
More informationSingle machine parallel batch scheduling with unbounded capacity
Workshop on Combinatorics and Graph Theory 21th, April, 2006 Nankai University Single machine parallel batch scheduling with unbounded capacity Yuan Jinjiang Department of mathematics, Zhengzhou University
More informationEvery tree contains a large induced subgraph with all degrees odd
Every tree contains a large induced subgraph with all degrees odd A.J. Radcliffe Carnegie Mellon University, Pittsburgh, PA A.D. Scott Department of Pure Mathematics and Mathematical Statistics University
More information3. The Junction Tree Algorithms
A Short Course on Graphical Models 3. The Junction Tree Algorithms Mark Paskin mark@paskin.org 1 Review: conditional independence Two random variables X and Y are independent (written X Y ) iff p X ( )
More informationReductions & NPcompleteness as part of Foundations of Computer Science undergraduate course
Reductions & NPcompleteness as part of Foundations of Computer Science undergraduate course Alex Angelopoulos, NTUA January 22, 2015 Outline Alex Angelopoulos (NTUA) FoCS: Reductions & NPcompleteness
More informationMany algorithms, particularly divide and conquer algorithms, have time complexities which are naturally
Recurrence Relations Many algorithms, particularly divide and conquer algorithms, have time complexities which are naturally modeled by recurrence relations. A recurrence relation is an equation which
More informationData Structures and Algorithms Written Examination
Data Structures and Algorithms Written Examination 22 February 2013 FIRST NAME STUDENT NUMBER LAST NAME SIGNATURE Instructions for students: Write First Name, Last Name, Student Number and Signature where
More informationOrdered Lists and Binary Trees
Data Structures and Algorithms Ordered Lists and Binary Trees Chris Brooks Department of Computer Science University of San Francisco Department of Computer Science University of San Francisco p.1/62 60:
More informationCAD Algorithms. P and NP
CAD Algorithms The Classes P and NP Mohammad Tehranipoor ECE Department 6 September 2010 1 P and NP P and NP are two families of problems. P is a class which contains all of the problems we solve using
More informationDATA ANALYSIS II. Matrix Algorithms
DATA ANALYSIS II Matrix Algorithms Similarity Matrix Given a dataset D = {x i }, i=1,..,n consisting of n points in R d, let A denote the n n symmetric similarity matrix between the points, given as where
More informationUniversality in the theory of algorithms and computer science
Universality in the theory of algorithms and computer science Alexander Shen Computational models The notion of computable function was introduced in 1930ies. Simplifying (a rather interesting and puzzling)
More informationPriority Based Enhancement of Online PowerAware Routing in Wireless Sensor Network. Ronit Nossenson Jerusalem College of Technology
Priority Based Enhancement of Online PowerAware Routing in Wireless Sensor Network Ronit Nossenson Jerusalem College of Technology COMCAS 2011 1 What are sensor networks? Infrastructureless networks
More informationBinary Search Trees. A Generic Tree. Binary Trees. Nodes in a binary search tree ( BST) are of the form. P parent. Key. Satellite data L R
Binary Search Trees A Generic Tree Nodes in a binary search tree ( BST) are of the form P parent Key A Satellite data L R B C D E F G H I J The BST has a root node which is the only node whose parent
More informationMethod To Solve Linear, Polynomial, or Absolute Value Inequalities:
Solving Inequalities An inequality is the result of replacing the = sign in an equation with ,, or. For example, 3x 2 < 7 is a linear inequality. We call it linear because if the < were replaced with
More informationDynamic Programming. Lecture 11. 11.1 Overview. 11.2 Introduction
Lecture 11 Dynamic Programming 11.1 Overview Dynamic Programming is a powerful technique that allows one to solve many different types of problems in time O(n 2 ) or O(n 3 ) for which a naive approach
More information5.1 Bipartite Matching
CS787: Advanced Algorithms Lecture 5: Applications of Network Flow In the last lecture, we looked at the problem of finding the maximum flow in a graph, and how it can be efficiently solved using the FordFulkerson
More informationA NOTE ON OFFDIAGONAL SMALL ONLINE RAMSEY NUMBERS FOR PATHS
A NOTE ON OFFDIAGONAL SMALL ONLINE RAMSEY NUMBERS FOR PATHS PAWE L PRA LAT Abstract. In this note we consider the online Ramsey numbers R(P n, P m ) for paths. Using a high performance computing clusters,
More informationScheduling Shop Scheduling. Tim Nieberg
Scheduling Shop Scheduling Tim Nieberg Shop models: General Introduction Remark: Consider non preemptive problems with regular objectives Notation Shop Problems: m machines, n jobs 1,..., n operations
More informationOne last point: we started off this book by introducing another famously hard search problem:
S. Dasgupta, C.H. Papadimitriou, and U.V. Vazirani 261 Factoring One last point: we started off this book by introducing another famously hard search problem: FACTORING, the task of finding all prime factors
More informationBinary Trees and Huffman Encoding Binary Search Trees
Binary Trees and Huffman Encoding Binary Search Trees Computer Science E119 Harvard Extension School Fall 2012 David G. Sullivan, Ph.D. Motivation: Maintaining a Sorted Collection of Data A data dictionary
More informationDynamic programming. Doctoral course Optimization on graphs  Lecture 4.1. Giovanni Righini. January 17 th, 2013
Dynamic programming Doctoral course Optimization on graphs  Lecture.1 Giovanni Righini January 1 th, 201 Implicit enumeration Combinatorial optimization problems are in general NPhard and we usually
More informationData Structures and Algorithms
Data Structures and Algorithms CS2452016S06 Binary Search Trees David Galles Department of Computer Science University of San Francisco 060: Ordered List ADT Operations: Insert an element in the list
More informationJava Software Structures
INTERNATIONAL EDITION Java Software Structures Designing and Using Data Structures FOURTH EDITION John Lewis Joseph Chase This page is intentionally left blank. Java Software Structures,International Edition
More informationNotes on NP Completeness
Notes on NP Completeness Rich Schwartz November 10, 2013 1 Overview Here are some notes which I wrote to try to understand what NP completeness means. Most of these notes are taken from Appendix B in Douglas
More information! Solve problem to optimality. ! Solve problem in polytime. ! Solve arbitrary instances of the problem. #approximation algorithm.
Approximation Algorithms 11 Approximation Algorithms Q Suppose I need to solve an NPhard problem What should I do? A Theory says you're unlikely to find a polytime algorithm Must sacrifice one of three
More informationAnalysis of Algorithms, I
Analysis of Algorithms, I CSOR W4231.002 Eleni Drinea Computer Science Department Columbia University Thursday, February 26, 2015 Outline 1 Recap 2 Representing graphs 3 Breadthfirst search (BFS) 4 Applications
More informationCpt S 223. School of EECS, WSU
The Shortest Path Problem 1 ShortestPath Algorithms Find the shortest path from point A to point B Shortest in time, distance, cost, Numerous applications Map navigation Flight itineraries Circuit wiring
More information2. FINDING A SOLUTION
The 7 th Balan Conference on Operational Research BACOR 5 Constanta, May 5, Roania OPTIMAL TIME AND SPACE COMPLEXITY ALGORITHM FOR CONSTRUCTION OF ALL BINARY TREES FROM PREORDER AND POSTORDER TRAVERSALS
More informationWelcome to... Problem Analysis and Complexity Theory 716.054, 3 VU
Welcome to... Problem Analysis and Complexity Theory 716.054, 3 VU Birgit Vogtenhuber Institute for Software Technology email: bvogt@ist.tugraz.at office hour: Tuesday 10:30 11:30 slides: http://www.ist.tugraz.at/pact.html
More informationIntroduction to Algorithms. Part 3: P, NP Hard Problems
Introduction to Algorithms Part 3: P, NP Hard Problems 1) Polynomial Time: P and NP 2) NPCompleteness 3) Dealing with Hard Problems 4) Lower Bounds 5) Books c Wayne Goddard, Clemson University, 2004 Chapter
More informationThe Basics of Graphical Models
The Basics of Graphical Models David M. Blei Columbia University October 3, 2015 Introduction These notes follow Chapter 2 of An Introduction to Probabilistic Graphical Models by Michael Jordan. Many figures
More informationExponential time algorithms for graph coloring
Exponential time algorithms for graph coloring Uriel Feige Lecture notes, March 14, 2011 1 Introduction Let [n] denote the set {1,..., k}. A klabeling of vertices of a graph G(V, E) is a function V [k].
More informationTheoretical Computer Science (Bridging Course) Complexity
Theoretical Computer Science (Bridging Course) Complexity Gian Diego Tipaldi A scenario You are a programmer working for a logistics company Your boss asks you to implement a program that optimizes the
More informationV. Adamchik 1. Graph Theory. Victor Adamchik. Fall of 2005
V. Adamchik 1 Graph Theory Victor Adamchik Fall of 2005 Plan 1. Basic Vocabulary 2. Regular graph 3. Connectivity 4. Representing Graphs Introduction A.Aho and J.Ulman acknowledge that Fundamentally, computer
More informationBinary Search Trees (BST)
Binary Search Trees (BST) 1. Hierarchical data structure with a single reference to node 2. Each node has at most two child nodes (a left and a right child) 3. Nodes are organized by the Binary Search
More informationStatic Load Balancing
Load Balancing Load Balancing Load balancing: distributing data and/or computations across multiple processes to maximize efficiency for a parallel program. Static loadbalancing: the algorithm decides
More informationJUSTINTIME SCHEDULING WITH PERIODIC TIME SLOTS. Received December May 12, 2003; revised February 5, 2004
Scientiae Mathematicae Japonicae Online, Vol. 10, (2004), 431 437 431 JUSTINTIME SCHEDULING WITH PERIODIC TIME SLOTS Ondřej Čepeka and Shao Chin Sung b Received December May 12, 2003; revised February
More informationCourse: Model, Learning, and Inference: Lecture 5
Course: Model, Learning, and Inference: Lecture 5 Alan Yuille Department of Statistics, UCLA Los Angeles, CA 90095 yuille@stat.ucla.edu Abstract Probability distributions on structured representation.
More informationA permutation can also be represented by describing its cycles. What do you suppose is meant by this?
Shuffling, Cycles, and Matrices Warm up problem. Eight people stand in a line. From left to right their positions are numbered,,,... 8. The eight people then change places according to THE RULE which directs
More informationThe Traveling Beams Optical Solutions for Bounded NPComplete Problems
The Traveling Beams Optical Solutions for Bounded NPComplete Problems Shlomi Dolev, Hen Fitoussi Abstract Architectures for optical processors designed to solve bounded instances of NPComplete problems
More informationAlgorithm Homework and Test Problems
Algorithm Homework and Test Problems Steven S. Skiena Department of Computer Science State University of New York Stony Brook, NY 117944400 skiena@cs.sunysb.edu January 29, 2006 All of the midterm and
More informationAtmiya Infotech Pvt. Ltd. Data Structure. By Ajay Raiyani. Yogidham, Kalawad Road, Rajkot. Ph : 572365, 576681 1
Data Structure By Ajay Raiyani Yogidham, Kalawad Road, Rajkot. Ph : 572365, 576681 1 Linked List 4 Singly Linked List...4 Doubly Linked List...7 Explain Doubly Linked list: ...7 Circular Singly Linked
More informationTREE BASIC TERMINOLOGIES
TREE Trees are very flexible, versatile and powerful nonliner data structure that can be used to represent data items possessing hierarchical relationship between the grand father and his children and
More informationInternational Journal of Software and Web Sciences (IJSWS) www.iasir.net
International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research) ISSN (Print): 22790063 ISSN (Online): 22790071 International
More informationSIMS 255 Foundations of Software Design. Complexity and NPcompleteness
SIMS 255 Foundations of Software Design Complexity and NPcompleteness Matt Welsh November 29, 2001 mdw@cs.berkeley.edu 1 Outline Complexity of algorithms Space and time complexity ``Big O'' notation Complexity
More informationScheduling Home Health Care with Separating Benders Cuts in Decision Diagrams
Scheduling Home Health Care with Separating Benders Cuts in Decision Diagrams André Ciré University of Toronto John Hooker Carnegie Mellon University INFORMS 2014 Home Health Care Home health care delivery
More informationSample Questions Csci 1112 A. Bellaachia
Sample Questions Csci 1112 A. Bellaachia Important Series : o S( N) 1 2 N N i N(1 N) / 2 i 1 o Sum of squares: N 2 N( N 1)(2N 1) N i for large N i 1 6 o Sum of exponents: N k 1 k N i for large N and k
More informationIntroduction to Logic in Computer Science: Autumn 2006
Introduction to Logic in Computer Science: Autumn 2006 Ulle Endriss Institute for Logic, Language and Computation University of Amsterdam Ulle Endriss 1 Plan for Today Now that we have a basic understanding
More informationLecture 1: Course overview, circuits, and formulas
Lecture 1: Course overview, circuits, and formulas Topics in Complexity Theory and Pseudorandomness (Spring 2013) Rutgers University Swastik Kopparty Scribes: John Kim, Ben Lund 1 Course Information Swastik
More informationLecture 15 An Arithmetic Circuit Lowerbound and Flows in Graphs
CSE599s: Extremal Combinatorics November 21, 2011 Lecture 15 An Arithmetic Circuit Lowerbound and Flows in Graphs Lecturer: Anup Rao 1 An Arithmetic Circuit Lower Bound An arithmetic circuit is just like
More informationNPCompleteness and Cook s Theorem
NPCompleteness and Cook s Theorem Lecture notes for COM3412 Logic and Computation 15th January 2002 1 NP decision problems The decision problem D L for a formal language L Σ is the computational task:
More informationCS473  Algorithms I
CS473  Algorithms I Lecture 4 The DivideandConquer Design Paradigm View in slideshow mode 1 Reminder: Merge Sort Input array A sort this half sort this half Divide Conquer merge two sorted halves Combine
More informationApplied Algorithm Design Lecture 5
Applied Algorithm Design Lecture 5 Pietro Michiardi Eurecom Pietro Michiardi (Eurecom) Applied Algorithm Design Lecture 5 1 / 86 Approximation Algorithms Pietro Michiardi (Eurecom) Applied Algorithm Design
More informationDATA STRUCTURES NOTES FOR THE FINAL EXAM SUMMER 2002 Michael Knopf mknopf@ufl.edu
DATA STRUCTURES NOTES FOR THE FINAL EXAM SUMMER 2002 Michael Knopf mknopf@ufl.edu ',6&/$,0(5 0U 0LFKDHO.QRSI SUHSDUHG WKHVH QRWHV 1HLWKHU WKH FRXUVH LQVWUXFWRU QRU WKH WHDFKLQJ DVVLVWDQWV KDYH UHYLHZHG
More informationThe Goldberg Rao Algorithm for the Maximum Flow Problem
The Goldberg Rao Algorithm for the Maximum Flow Problem COS 528 class notes October 18, 2006 Scribe: Dávid Papp Main idea: use of the blocking flow paradigm to achieve essentially O(min{m 2/3, n 1/2 }
More information1. The memory address of the first element of an array is called A. floor address B. foundation addressc. first address D.
1. The memory address of the first element of an array is called A. floor address B. foundation addressc. first address D. base address 2. The memory address of fifth element of an array can be calculated
More information