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The document describes the traveling salesperson problem and the lazy constraint branch and bound (LCBB) algorithm to solve it. It explains that LCBB uses a state space tree to represent solutions, with each leaf node representing a tour. It provides details on how LCBB works, including defining cost, upper bound, and estimation functions. It also discusses an alternative tree organization where tours are represented as collections of edges included/excluded at each node.

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Data structure - traveling sales person and mesh algorithm

This document discusses the traveling salesperson problem and mesh algorithms. It introduces:
1) A dynamic programming algorithm for solving the traveling salesperson problem that uses a state space tree representation. The algorithm finds the optimal tour by exploring the tree.
2) How to represent the traveling salesperson problem on a mesh-connected computer, with processors arranged in a square grid. Basic operations on the mesh take unit time.
3) The mesh algorithm works by having each processor row or column perform local computations analogous to exploring the state space tree in a linear array fashion.

Parallel Evaluation of Multi-Semi-Joins

Presentation given on VLDB 2016: 42nd International Conference on Very Large Data Bases.
Paper: http://dx.doi.org/10.14778/2977797.2977800
ArXiv: https://arxiv.org/abs/1605.05219
Poster: https://zenodo.org/record/61653 (doi 10.5281/zenodo.61653)
Gumbo Software: https://github.com/JonnyDaenen/Gumbo
Abstract
While services such as Amazon AWS make computing power abundantly available, adding more computing nodes can incur high costs in, for instance, pay-as-you-go plans while not always significantly improving the net running time (aka wall-clock time) of queries. In this work, we provide algorithms for parallel evaluation of SGF queries in MapReduce that optimize total time, while retaining low net time. Not only can SGF queries specify all semi-join reducers, but also more expressive queries involving disjunction and negation. Since SGF queries can be seen as Boolean combinations of (potentially nested) semi-joins, we introduce a novel multi-semi-join (MSJ) MapReduce operator that enables the evaluation of a set of semi-joins in one job. We use this operator to obtain parallel query plans for SGF queries that outvalue sequential plans w.r.t. net time and provide additional optimizations aimed at minimizing total time without severely affecting net time. Even though the latter optimizations are NP-hard, we present effective greedy algorithms. Our experiments, conducted using our own implementation Gumbo on top of Hadoop, confirm the usefulness of parallel query plans, and the effectiveness and scalability of our optimizations, all with a significant improvement over Pig and Hive.

Cs6402 design and analysis of algorithms may june 2016 answer key

The document discusses algorithms and complexity analysis. It provides Euclid's algorithm for computing greatest common divisor, compares the orders of growth of n(n-1)/2 and n^2, and describes the general strategy of divide and conquer methods. It also defines problems like the closest pair problem, single source shortest path problem, and assignment problem. Finally, it discusses topics like state space trees, the extreme point theorem, and lower bounds.

Problem Solving by Computer Finite Element Method

This document discusses using finite element methods and the cotangent Laplacian to solve partial differential equations numerically. It begins by explaining how to generate simplicial meshes by dividing a region into basic pieces. It then introduces the cotangent Laplacian, which approximates the Laplacian operator, and how it is calculated based on angles in triangles. Finally, it demonstrates applying the cotangent Laplacian to solve sample Dirichlet and Neumann boundary value problems and compares the approximate solutions to exact solutions, showing convergence as the mesh is refined.

algorithm Unit 3

This document discusses dynamic programming and algorithms for solving all-pair shortest path problems. It begins by defining dynamic programming as avoiding recalculating solutions by storing results in a table. It then describes Floyd's algorithm for finding shortest paths between all pairs of nodes in a graph. The algorithm iterates through nodes, calculating shortest paths that pass through each intermediate node. It takes O(n3) time for a graph with n nodes. Finally, it discusses the multistage graph problem and provides forward and backward algorithms to find the minimum cost path from source to destination in a multistage graph in O(V+E) time, where V and E are the numbers of vertices and edges.

Nbhm m. a. and m.sc. scholarship test 2011

The document provides instructions for a mathematics scholarship test consisting of 3 sections (Algebra, Analysis, Geometry) with 10 questions each. It defines key terms and notations used in the test, such as types of matrices, function notation, and interval notation. It also specifies rules for the test, including that calculators are not allowed and that points will only be awarded if all choices in a question are correct.

Graphs

The document discusses graphs and graph algorithms. It defines what a graph is - a non-linear data structure consisting of vertices connected by edges. It describes different graph representations like adjacency matrix, incidence matrix and adjacency list. It also explains graph traversal algorithms like depth-first search and breadth-first search. Finally, it covers minimum spanning tree algorithms like Kruskal's and Prim's algorithms for finding minimum cost spanning trees in graphs.

Introductory Algebra Lesson 11 โ Linear Functions, Part 2 .docx

Introductory Algebra Lesson 11 โ Linear Functions, Part 2
Practice Problems
Skills Practice
1. Determine the slope-intercept form of the equation of the line between each of the following
pairs of points.
a. (4, -5) and (2, 3)
b. (-3, 2) and (1, 8)
c. (5, -9) and (5, 2)
d. (2, -1) and (-2, 3)
e. (4, 3) and (12, -3)
f. (2, -4) and (7, -4)
Introductory Algebra Lesson 11 โ Linear Functions, Part 2
2. Give the equation of the linear function that generates the following table of values. Write
your answer in slope-intercept form.
x f (x)
-5 91
-2 67
1 43
4 19
9 -21
3. Give the equation of the linear function that generates the following table of values. Write
your answer in slope-intercept form.
t C(t)
5 -1250
15 -900
20 -725
35 -200
45 150
4. Give the equation of the linear function shown below. Write your answer in slope-intercept
form.
Introductory Algebra Lesson 11 โ Linear Functions, Part 2
5. Give the equation of the linear function shown below. Write your answer in slope-intercept
form.
6. Give the equation of the linear function shown below. Write your answer in slope-intercept
form.
7. Give the equation of the linear function shown below. Write your answer in slope-intercept
form.
Introductory Algebra Lesson 11 โ Linear Functions, Part 2
8. Give the equation of the linear function shown below. Write your answer in slope-intercept
form.
9. Give the equation of the linear function shown below. Write your answer in slope-intercept
form.
10. Give the equation of the linear function shown below. Write your answer in slope-intercept
form.
Introductory Algebra Lesson 11 โ Linear Functions, Part 2
11. Give the equation of the horizontal line passing through the point (-6, 11). _______________
12. Give the equation of the vertical line passing through the point (4, 7). _______________
13. Give the equation of the x-axis. _______________
14. Give the equation of the y-axis. _______________
15. Give the equation of the line passing through the point (1, -5) that is parallel to y = 12 โ 8x.
16. Give the equation of the line passing through the point (6, 0) that is parallel to y = x
2
3
9 ๏ญ .
17. Give the equation of the line passing through the point (10, 3) that is perpendicular to
1
5
2
๏ซ๏ฝ xy .
18. Give the equation of the line passing through the point (-12, -1) that is perpendicular to
xy 43๏ญ๏ฝ .
Introductory Algebra Lesson 11 โ Linear Functions, Part 2
19. Draw an accurate graph of the linear equation 2x + 3y = 6.
Slope-Intercept Form:
Slope: ___________
Vertical Intercept: ____________
Horizontal Intercept: ____________
Two additional points on the line:
____________ _____________
20. Draw an accurate graph of the function 155 ๏ฝ๏ซ yx
Slope-In.

Data structure - traveling sales person and mesh algorithm

This document discusses the traveling salesperson problem and mesh algorithms. It introduces:
1) A dynamic programming algorithm for solving the traveling salesperson problem that uses a state space tree representation. The algorithm finds the optimal tour by exploring the tree.
2) How to represent the traveling salesperson problem on a mesh-connected computer, with processors arranged in a square grid. Basic operations on the mesh take unit time.
3) The mesh algorithm works by having each processor row or column perform local computations analogous to exploring the state space tree in a linear array fashion.

Parallel Evaluation of Multi-Semi-Joins

Presentation given on VLDB 2016: 42nd International Conference on Very Large Data Bases.
Paper: http://dx.doi.org/10.14778/2977797.2977800
ArXiv: https://arxiv.org/abs/1605.05219
Poster: https://zenodo.org/record/61653 (doi 10.5281/zenodo.61653)
Gumbo Software: https://github.com/JonnyDaenen/Gumbo
Abstract
While services such as Amazon AWS make computing power abundantly available, adding more computing nodes can incur high costs in, for instance, pay-as-you-go plans while not always significantly improving the net running time (aka wall-clock time) of queries. In this work, we provide algorithms for parallel evaluation of SGF queries in MapReduce that optimize total time, while retaining low net time. Not only can SGF queries specify all semi-join reducers, but also more expressive queries involving disjunction and negation. Since SGF queries can be seen as Boolean combinations of (potentially nested) semi-joins, we introduce a novel multi-semi-join (MSJ) MapReduce operator that enables the evaluation of a set of semi-joins in one job. We use this operator to obtain parallel query plans for SGF queries that outvalue sequential plans w.r.t. net time and provide additional optimizations aimed at minimizing total time without severely affecting net time. Even though the latter optimizations are NP-hard, we present effective greedy algorithms. Our experiments, conducted using our own implementation Gumbo on top of Hadoop, confirm the usefulness of parallel query plans, and the effectiveness and scalability of our optimizations, all with a significant improvement over Pig and Hive.

Cs6402 design and analysis of algorithms may june 2016 answer key

The document discusses algorithms and complexity analysis. It provides Euclid's algorithm for computing greatest common divisor, compares the orders of growth of n(n-1)/2 and n^2, and describes the general strategy of divide and conquer methods. It also defines problems like the closest pair problem, single source shortest path problem, and assignment problem. Finally, it discusses topics like state space trees, the extreme point theorem, and lower bounds.

Problem Solving by Computer Finite Element Method

This document discusses using finite element methods and the cotangent Laplacian to solve partial differential equations numerically. It begins by explaining how to generate simplicial meshes by dividing a region into basic pieces. It then introduces the cotangent Laplacian, which approximates the Laplacian operator, and how it is calculated based on angles in triangles. Finally, it demonstrates applying the cotangent Laplacian to solve sample Dirichlet and Neumann boundary value problems and compares the approximate solutions to exact solutions, showing convergence as the mesh is refined.

algorithm Unit 3

This document discusses dynamic programming and algorithms for solving all-pair shortest path problems. It begins by defining dynamic programming as avoiding recalculating solutions by storing results in a table. It then describes Floyd's algorithm for finding shortest paths between all pairs of nodes in a graph. The algorithm iterates through nodes, calculating shortest paths that pass through each intermediate node. It takes O(n3) time for a graph with n nodes. Finally, it discusses the multistage graph problem and provides forward and backward algorithms to find the minimum cost path from source to destination in a multistage graph in O(V+E) time, where V and E are the numbers of vertices and edges.

Nbhm m. a. and m.sc. scholarship test 2011

The document provides instructions for a mathematics scholarship test consisting of 3 sections (Algebra, Analysis, Geometry) with 10 questions each. It defines key terms and notations used in the test, such as types of matrices, function notation, and interval notation. It also specifies rules for the test, including that calculators are not allowed and that points will only be awarded if all choices in a question are correct.

Graphs

The document discusses graphs and graph algorithms. It defines what a graph is - a non-linear data structure consisting of vertices connected by edges. It describes different graph representations like adjacency matrix, incidence matrix and adjacency list. It also explains graph traversal algorithms like depth-first search and breadth-first search. Finally, it covers minimum spanning tree algorithms like Kruskal's and Prim's algorithms for finding minimum cost spanning trees in graphs.

Introductory Algebra Lesson 11 โ Linear Functions, Part 2 .docx

Introductory Algebra Lesson 11 โ Linear Functions, Part 2
Practice Problems
Skills Practice
1. Determine the slope-intercept form of the equation of the line between each of the following
pairs of points.
a. (4, -5) and (2, 3)
b. (-3, 2) and (1, 8)
c. (5, -9) and (5, 2)
d. (2, -1) and (-2, 3)
e. (4, 3) and (12, -3)
f. (2, -4) and (7, -4)
Introductory Algebra Lesson 11 โ Linear Functions, Part 2
2. Give the equation of the linear function that generates the following table of values. Write
your answer in slope-intercept form.
x f (x)
-5 91
-2 67
1 43
4 19
9 -21
3. Give the equation of the linear function that generates the following table of values. Write
your answer in slope-intercept form.
t C(t)
5 -1250
15 -900
20 -725
35 -200
45 150
4. Give the equation of the linear function shown below. Write your answer in slope-intercept
form.
Introductory Algebra Lesson 11 โ Linear Functions, Part 2
5. Give the equation of the linear function shown below. Write your answer in slope-intercept
form.
6. Give the equation of the linear function shown below. Write your answer in slope-intercept
form.
7. Give the equation of the linear function shown below. Write your answer in slope-intercept
form.
Introductory Algebra Lesson 11 โ Linear Functions, Part 2
8. Give the equation of the linear function shown below. Write your answer in slope-intercept
form.
9. Give the equation of the linear function shown below. Write your answer in slope-intercept
form.
10. Give the equation of the linear function shown below. Write your answer in slope-intercept
form.
Introductory Algebra Lesson 11 โ Linear Functions, Part 2
11. Give the equation of the horizontal line passing through the point (-6, 11). _______________
12. Give the equation of the vertical line passing through the point (4, 7). _______________
13. Give the equation of the x-axis. _______________
14. Give the equation of the y-axis. _______________
15. Give the equation of the line passing through the point (1, -5) that is parallel to y = 12 โ 8x.
16. Give the equation of the line passing through the point (6, 0) that is parallel to y = x
2
3
9 ๏ญ .
17. Give the equation of the line passing through the point (10, 3) that is perpendicular to
1
5
2
๏ซ๏ฝ xy .
18. Give the equation of the line passing through the point (-12, -1) that is perpendicular to
xy 43๏ญ๏ฝ .
Introductory Algebra Lesson 11 โ Linear Functions, Part 2
19. Draw an accurate graph of the linear equation 2x + 3y = 6.
Slope-Intercept Form:
Slope: ___________
Vertical Intercept: ____________
Horizontal Intercept: ____________
Two additional points on the line:
____________ _____________
20. Draw an accurate graph of the function 155 ๏ฝ๏ซ yx
Slope-In.

5.vector geometry Further Mathematics Zimbabwe Zimsec Cambridge

vector geometry Further Mathematics Zimbabwe Zimsec Cambridge
Zimsec
Zimbabwe
Alpro Elearning Portal

graph theory

This document provides an introduction to graph theory concepts. It defines what a graph is consisting of vertices and edges. It discusses different types of graphs like simple graphs, multigraphs, digraphs and their properties. It introduces concepts like degrees of vertices, handshaking lemma, planar graphs, Euler's formula, bipartite graphs and graph coloring. It provides examples of special graphs like complete graphs, cycles, wheels and hypercubes. It discusses applications of graphs in areas like job assignments and local area networks. The document also summarizes theorems regarding planar graphs like Kuratowski's theorem stating conditions for a graph to be non-planar.

02 linear algebra

This document provides an overview of key concepts in linear algebra that are relevant for deep learning, including:
- Vectors are 1-D arrays of numbers that can be represented as points in space. Matrices are 2-D arrays where each element is identified by two indices. Tensors generalize this to arrays with more than two axes.
- Operations like matrix multiplication and transposition are defined. The dot product of two vectors or matrices is also introduced.
- Systems of linear equations can be represented using matrix-vector notation. Matrix inversion allows solving such systems, though it is numerically unstable.
- Norms are functions that measure the "size" of vectors and are useful in machine learning,

02 linear algebra

The document summarizes key concepts from chapter 2 of the lecture slides on linear algebra for deep learning. It defines scalars as single numbers and vectors as 1-D arrays of numbers that can be indexed. Matrices are 2-D arrays of numbers that are indexed with two numbers. Tensors generalize this to arrays with more dimensions. The document also discusses matrix operations like transpose, dot product, and inversion which are important for solving systems of linear equations. It introduces norms as functions to measure the size of vectors.

Ijcatr03051008Implementation of Matrix based Mapping Method Using Elliptic Cu...

Elliptic Curve Cryptography (ECC) gained a lot of attention in industry. The key attraction of ECC over RSA is that it
offers equal security even for smaller bit size, thus reducing the processing complexity. ECC Encryption and Decryption methods can
only perform encrypt and decrypt operations on the curve but not on the message. This paper presents a fast mapping method based on
matrix approach for ECC, which offers high security for the encrypted message. First, the alphabetic message is mapped on to the
points on an elliptic curve. Later encode those points using Elgamal encryption method with the use of a non-singular matrix. And the
encoded message can be decrypted by Elgamal decryption technique and to get back the original message, the matrix obtained from
decoding is multiplied with the inverse of non-singular matrix. The coding is done using Verilog. The design is simulated and
synthesized using FPGA.

Exponential-Function.pptx general Mathematics

This document covers exponential functions including:
- Representing exponential functions through tables, graphs, and equations.
- Finding the domain, range, intercepts, zeros, and asymptotes of exponential functions.
- Graphing exponential functions and describing how they increase or decrease based on the base value.
- Transformations of exponential functions including reflections across axes and translations.
- Properties of natural exponential functions where e is the base.

matlab functions

The document describes functions and exercises for basic simulation and matrix manipulation in MATLAB. It covers creating vectors and matrices, arithmetic operations, matrix manipulations like concatenation and indexing, sorting, shifting, reshaping and flipping matrices. It also discusses generating random sequences, plotting functions, solving differential equations, and creating and accessing structures and arrays of structures. The key topics are functions for vector/matrix creation and manipulation, common mathematical operations, plotting and solving differential equations in MATLAB.

Cs6660 compiler design november december 2016 Answer key

The document discusses topics related to compiler design, including:
1) The phases of a compiler include lexical analysis, syntax analysis, semantic analysis, intermediate code generation, code optimization, and code generation. Compiler construction tools help implement these phases.
2) Grouping compiler phases can improve efficiency. Errors can occur in all phases, from syntax errors to type errors.
3) Questions cover topics like symbol tables, finite automata in lexical analysis, parse trees, ambiguity, SLR parsing, syntax directed translations, code generation, and optimization techniques like loop detection.

Graph in Data Structure

A graph G consists of a non empty set V called the set of nodes (points, vertices) of the graph, a set E, which is the set of edges of the graph and a mapping from the set of edges E to a pair of elements of V.
Any two nodes, which are connected by an edge in a graph are called "adjacent nodes".
In a graph G(V,E) an edge which is directed from one node to another is called a "directed edge", while an edge which has no specific direction is called an "undirected edge". A graph in which every edge is directed is called a "directed graph" or a "digraph". A graph in which every edge is undirected is called an "undirected graph".
If some of edges are directed and some are undirected in a graph then the graph is called a "mixed graph".
Any graph which contains some parallel edges is called a "multigraph".
If there is no more than one edge but a pair of nodes then, such a graph is called "simple graph."
A graph in which weights are assigned to every edge is called a "weighted graph".
In a graph, a node which is not adjacent to any other node is called "isolated node".
A graph containing only isolated nodes is called a "null graph". In a directed graph for any node v the number of edges which have v as initial node is called the "outdegree" of the node v. The number of edges to have v as their terminal node is called the "Indegree" of v and Sum of outdegree and indegree of a node v is called its total degree.

Unit 3 daa

This document discusses dynamic programming and algorithms for solving all-pair shortest path problems. It begins by explaining dynamic programming as an optimization technique that works bottom-up by solving subproblems once and storing their solutions, rather than recomputing them. It then presents Floyd's algorithm for finding shortest paths between all pairs of nodes in a graph. The algorithm iterates through nodes, updating the shortest path lengths between all pairs that include that node by exploring paths through it. Finally, it discusses solving multistage graph problems using forward and backward methods that work through the graph stages in different orders.

Nbhm m. a. and m.sc. scholarship test 2012 with answer key

The document provides instructions for a mathematics scholarship test consisting of 30 questions across three sections: Algebra, Analysis, and Geometry. It outlines the structure and time limit of the test, how to answer questions, notations that will be used, and clarifies that calculators are not allowed. The key at the end provides the answers to sample questions asked in each section to illustrate the nature and difficulty of the test.

Beginning direct3d gameprogrammingmath05_matrices_20160515_jintaeks

This document provides an overview of linear systems and matrices. It defines key linear algebra concepts such as linear functions, linear maps, homogeneous and non-homogeneous linear systems, and plane equations. It also explains how to represent linear systems using matrices and describes common matrix operations including addition, scalar multiplication, transposition, and matrix multiplication. Finally, it discusses inverses, determinants, and using matrices to represent transformations such as rotations in 2D space.

10.1.1.630.8055

This document summarizes a paper that presents new algorithms for solving the cyclic order-preserving assignment problem (COPAP) and related sub-problem, the linear order-preserving assignment problem (LOPAP). It introduces a new point-assignment cost function called the Procrustean local shape distance (PLSD) and explores heuristics for using the A* search algorithm to more efficiently solve COPAP and LOPAP. Experimental results on the MPEG-7 shape dataset are presented and recommendations are made for solving COPAP/LOPAP in practice.

Daa chapter11

The document discusses approximation algorithms for NP-complete problems. It introduces the concept of approximation ratios, which measure how close an approximate solution from a polynomial-time algorithm is to the optimal solution. The document then provides examples of approximation algorithms with a ratio of 2 for the vertex cover and traveling salesman problems. It also discusses using backtracking to find all possible solutions to the subset sum problem.

Lecture_10_Parallel_Algorithms_Part_II.ppt

The document discusses parallel graph algorithms. It describes Dijkstra's algorithm for finding single-source shortest paths and its parallel formulations. It also describes Floyd's algorithm for finding all-pairs shortest paths and its parallel formulation using a 2D block mapping. Additionally, it discusses Johnson's algorithm, a modification of Dijkstra's algorithm to efficiently handle sparse graphs, and its parallel formulation.

Graph Introduction.ppt

A graph is a pair (V, E) where V is a set of vertices and E is a set of edges connecting the vertices. Graphs can be represented using an adjacency matrix, where a matrix element A[i,j] indicates if there is an edge from vertex i to j, or using adjacency lists, where each vertex stores a list of its neighboring vertices. Graphs find applications in modeling networks, databases, and more. Common graph operations include finding paths and connectivity between vertices.

Algorithms Design Exam Help

I am Dennis L. I am an Algorithm Design Exam Expert at programmingexamhelp.com. I hold a Ph.D. in Computer Science from, the City University of New York. I have been helping students with their exams for the past 7 years. You can hire me to take your exam in Algorithm Design.
Visit programmingexamhelp.com or email support@programmingexamhelp.com. You can also call on +1 678 648 4277 for any assistance with the Algorithm Design Exam.

Computer Network Homework Help

I am Isaac M. I am a Computer Network Assignment Expert at computernetworkassignmenthelp.com. I hold a Master's in Computer Science from, Glasgow University, UK. I have been helping students with their assignments for the past 8 years. I solve assignments related to the Computer Network.
Visit computernetworkassignmenthelp.com or email support@computernetworkassignmenthelp.com.
You can also call on +1 678 648 4277 for any assistance with the Computer Network Assignment.

Traveling Salesman Problem in Distributed Environment

In this paper, we focus on developing parallel algorithms for solving the traveling salesman problem (TSP) based on Nicos Christofides algorithm released in 1976. The parallel algorithm
is built in the distributed environment with multi-processors (Master-Slave). The algorithm is installed on the computer cluster system of National University of Education in Hanoi,
Vietnam (ccs1.hnue.edu.vn) and uses the library PJ (Parallel Java). The results are evaluated and compared with other works.

TRAVELING SALESMAN PROBLEM IN DISTRIBUTED ENVIRONMENT

The document describes developing a parallel algorithm for solving the traveling salesman problem (TSP) based on Christofides' algorithm. It discusses implementing Christofides' algorithm in a distributed environment using multiple processors. The parallel algorithm divides the graph vertices and distance matrix across slave processors, which calculate the minimum spanning tree in parallel. The master processor then finds odd-degree vertices, performs matching, and finds the Hamiltonian cycle to solve TSP. The algorithm is tested on a computer cluster using graphs of 20,000 and 30,000 nodes, showing improved runtime over the sequential algorithm.

type of edge detector.pptx

This document discusses different types of edge detectors used in digital image processing. It describes three types of edges - horizontal, vertical, and diagonal - and explains that edge detection identifies regions of discontinuity in an image. Several first-order gradient-based operators are covered, including Sobel, Prewitt, and Robert operators, as well as gaussian-based operators like the Laplacian of Gaussian and Canny edge detector. The Sobel, Prewitt, and Robert operators use kernels to calculate horizontal and vertical derivative approximations to detect edges. The Laplacian of Gaussian uses the second derivative and zero-crossing method. The Canny operator is an optimal technique that is not susceptible to noise.

helpline number in women empowerment.pptx

The document discusses India's Universalisation of Women Helpline Scheme, which aims to empower women through a toll-free helpline number. The key points are:
- The scheme provides a 24/7 helpline (181 number) to support women affected by violence through referral to police, hospitals, legal services and crisis centers.
- The helpline's objectives are to provide crisis intervention, information on support services and women's empowerment programs, and details of relevant laws and government schemes.
- Services include prevention of violence, emergency response, counseling, information on schemes and accessing benefits. High-risk cases are immediately referred to emergency services and authorities.

5.vector geometry Further Mathematics Zimbabwe Zimsec Cambridge

vector geometry Further Mathematics Zimbabwe Zimsec Cambridge
Zimsec
Zimbabwe
Alpro Elearning Portal

graph theory

This document provides an introduction to graph theory concepts. It defines what a graph is consisting of vertices and edges. It discusses different types of graphs like simple graphs, multigraphs, digraphs and their properties. It introduces concepts like degrees of vertices, handshaking lemma, planar graphs, Euler's formula, bipartite graphs and graph coloring. It provides examples of special graphs like complete graphs, cycles, wheels and hypercubes. It discusses applications of graphs in areas like job assignments and local area networks. The document also summarizes theorems regarding planar graphs like Kuratowski's theorem stating conditions for a graph to be non-planar.

02 linear algebra

This document provides an overview of key concepts in linear algebra that are relevant for deep learning, including:
- Vectors are 1-D arrays of numbers that can be represented as points in space. Matrices are 2-D arrays where each element is identified by two indices. Tensors generalize this to arrays with more than two axes.
- Operations like matrix multiplication and transposition are defined. The dot product of two vectors or matrices is also introduced.
- Systems of linear equations can be represented using matrix-vector notation. Matrix inversion allows solving such systems, though it is numerically unstable.
- Norms are functions that measure the "size" of vectors and are useful in machine learning,

02 linear algebra

The document summarizes key concepts from chapter 2 of the lecture slides on linear algebra for deep learning. It defines scalars as single numbers and vectors as 1-D arrays of numbers that can be indexed. Matrices are 2-D arrays of numbers that are indexed with two numbers. Tensors generalize this to arrays with more dimensions. The document also discusses matrix operations like transpose, dot product, and inversion which are important for solving systems of linear equations. It introduces norms as functions to measure the size of vectors.

Ijcatr03051008Implementation of Matrix based Mapping Method Using Elliptic Cu...

Elliptic Curve Cryptography (ECC) gained a lot of attention in industry. The key attraction of ECC over RSA is that it
offers equal security even for smaller bit size, thus reducing the processing complexity. ECC Encryption and Decryption methods can
only perform encrypt and decrypt operations on the curve but not on the message. This paper presents a fast mapping method based on
matrix approach for ECC, which offers high security for the encrypted message. First, the alphabetic message is mapped on to the
points on an elliptic curve. Later encode those points using Elgamal encryption method with the use of a non-singular matrix. And the
encoded message can be decrypted by Elgamal decryption technique and to get back the original message, the matrix obtained from
decoding is multiplied with the inverse of non-singular matrix. The coding is done using Verilog. The design is simulated and
synthesized using FPGA.

Exponential-Function.pptx general Mathematics

This document covers exponential functions including:
- Representing exponential functions through tables, graphs, and equations.
- Finding the domain, range, intercepts, zeros, and asymptotes of exponential functions.
- Graphing exponential functions and describing how they increase or decrease based on the base value.
- Transformations of exponential functions including reflections across axes and translations.
- Properties of natural exponential functions where e is the base.

matlab functions

The document describes functions and exercises for basic simulation and matrix manipulation in MATLAB. It covers creating vectors and matrices, arithmetic operations, matrix manipulations like concatenation and indexing, sorting, shifting, reshaping and flipping matrices. It also discusses generating random sequences, plotting functions, solving differential equations, and creating and accessing structures and arrays of structures. The key topics are functions for vector/matrix creation and manipulation, common mathematical operations, plotting and solving differential equations in MATLAB.

Cs6660 compiler design november december 2016 Answer key

The document discusses topics related to compiler design, including:
1) The phases of a compiler include lexical analysis, syntax analysis, semantic analysis, intermediate code generation, code optimization, and code generation. Compiler construction tools help implement these phases.
2) Grouping compiler phases can improve efficiency. Errors can occur in all phases, from syntax errors to type errors.
3) Questions cover topics like symbol tables, finite automata in lexical analysis, parse trees, ambiguity, SLR parsing, syntax directed translations, code generation, and optimization techniques like loop detection.

Graph in Data Structure

A graph G consists of a non empty set V called the set of nodes (points, vertices) of the graph, a set E, which is the set of edges of the graph and a mapping from the set of edges E to a pair of elements of V.
Any two nodes, which are connected by an edge in a graph are called "adjacent nodes".
In a graph G(V,E) an edge which is directed from one node to another is called a "directed edge", while an edge which has no specific direction is called an "undirected edge". A graph in which every edge is directed is called a "directed graph" or a "digraph". A graph in which every edge is undirected is called an "undirected graph".
If some of edges are directed and some are undirected in a graph then the graph is called a "mixed graph".
Any graph which contains some parallel edges is called a "multigraph".
If there is no more than one edge but a pair of nodes then, such a graph is called "simple graph."
A graph in which weights are assigned to every edge is called a "weighted graph".
In a graph, a node which is not adjacent to any other node is called "isolated node".
A graph containing only isolated nodes is called a "null graph". In a directed graph for any node v the number of edges which have v as initial node is called the "outdegree" of the node v. The number of edges to have v as their terminal node is called the "Indegree" of v and Sum of outdegree and indegree of a node v is called its total degree.

Unit 3 daa

This document discusses dynamic programming and algorithms for solving all-pair shortest path problems. It begins by explaining dynamic programming as an optimization technique that works bottom-up by solving subproblems once and storing their solutions, rather than recomputing them. It then presents Floyd's algorithm for finding shortest paths between all pairs of nodes in a graph. The algorithm iterates through nodes, updating the shortest path lengths between all pairs that include that node by exploring paths through it. Finally, it discusses solving multistage graph problems using forward and backward methods that work through the graph stages in different orders.

Nbhm m. a. and m.sc. scholarship test 2012 with answer key

The document provides instructions for a mathematics scholarship test consisting of 30 questions across three sections: Algebra, Analysis, and Geometry. It outlines the structure and time limit of the test, how to answer questions, notations that will be used, and clarifies that calculators are not allowed. The key at the end provides the answers to sample questions asked in each section to illustrate the nature and difficulty of the test.

Beginning direct3d gameprogrammingmath05_matrices_20160515_jintaeks

This document provides an overview of linear systems and matrices. It defines key linear algebra concepts such as linear functions, linear maps, homogeneous and non-homogeneous linear systems, and plane equations. It also explains how to represent linear systems using matrices and describes common matrix operations including addition, scalar multiplication, transposition, and matrix multiplication. Finally, it discusses inverses, determinants, and using matrices to represent transformations such as rotations in 2D space.

10.1.1.630.8055

This document summarizes a paper that presents new algorithms for solving the cyclic order-preserving assignment problem (COPAP) and related sub-problem, the linear order-preserving assignment problem (LOPAP). It introduces a new point-assignment cost function called the Procrustean local shape distance (PLSD) and explores heuristics for using the A* search algorithm to more efficiently solve COPAP and LOPAP. Experimental results on the MPEG-7 shape dataset are presented and recommendations are made for solving COPAP/LOPAP in practice.

Daa chapter11

The document discusses approximation algorithms for NP-complete problems. It introduces the concept of approximation ratios, which measure how close an approximate solution from a polynomial-time algorithm is to the optimal solution. The document then provides examples of approximation algorithms with a ratio of 2 for the vertex cover and traveling salesman problems. It also discusses using backtracking to find all possible solutions to the subset sum problem.

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The document discusses parallel graph algorithms. It describes Dijkstra's algorithm for finding single-source shortest paths and its parallel formulations. It also describes Floyd's algorithm for finding all-pairs shortest paths and its parallel formulation using a 2D block mapping. Additionally, it discusses Johnson's algorithm, a modification of Dijkstra's algorithm to efficiently handle sparse graphs, and its parallel formulation.

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A graph is a pair (V, E) where V is a set of vertices and E is a set of edges connecting the vertices. Graphs can be represented using an adjacency matrix, where a matrix element A[i,j] indicates if there is an edge from vertex i to j, or using adjacency lists, where each vertex stores a list of its neighboring vertices. Graphs find applications in modeling networks, databases, and more. Common graph operations include finding paths and connectivity between vertices.

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In this paper, we focus on developing parallel algorithms for solving the traveling salesman problem (TSP) based on Nicos Christofides algorithm released in 1976. The parallel algorithm
is built in the distributed environment with multi-processors (Master-Slave). The algorithm is installed on the computer cluster system of National University of Education in Hanoi,
Vietnam (ccs1.hnue.edu.vn) and uses the library PJ (Parallel Java). The results are evaluated and compared with other works.

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The document describes developing a parallel algorithm for solving the traveling salesman problem (TSP) based on Christofides' algorithm. It discusses implementing Christofides' algorithm in a distributed environment using multiple processors. The parallel algorithm divides the graph vertices and distance matrix across slave processors, which calculate the minimum spanning tree in parallel. The master processor then finds odd-degree vertices, performs matching, and finds the Hamiltonian cycle to solve TSP. The algorithm is tested on a computer cluster using graphs of 20,000 and 30,000 nodes, showing improved runtime over the sequential algorithm.

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Algorithms Design Exam Help

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type of edge detector.pptx

This document discusses different types of edge detectors used in digital image processing. It describes three types of edges - horizontal, vertical, and diagonal - and explains that edge detection identifies regions of discontinuity in an image. Several first-order gradient-based operators are covered, including Sobel, Prewitt, and Robert operators, as well as gaussian-based operators like the Laplacian of Gaussian and Canny edge detector. The Sobel, Prewitt, and Robert operators use kernels to calculate horizontal and vertical derivative approximations to detect edges. The Laplacian of Gaussian uses the second derivative and zero-crossing method. The Canny operator is an optimal technique that is not susceptible to noise.

helpline number in women empowerment.pptx

The document discusses India's Universalisation of Women Helpline Scheme, which aims to empower women through a toll-free helpline number. The key points are:
- The scheme provides a 24/7 helpline (181 number) to support women affected by violence through referral to police, hospitals, legal services and crisis centers.
- The helpline's objectives are to provide crisis intervention, information on support services and women's empowerment programs, and details of relevant laws and government schemes.
- Services include prevention of violence, emergency response, counseling, information on schemes and accessing benefits. High-risk cases are immediately referred to emergency services and authorities.

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This document discusses home automation and its components. Home automation allows users to control devices in their home like lights, fans, and TVs from anywhere using their phone or other connected devices. It can also include security features like alarms and cameras. A home automation system typically connects controlled devices to a central hub or gateway. Key components discussed are smart lighting, smart appliances, intrusion detection, and smoke/gas detectors. Smart lighting can save energy by adjusting lights based on movement. Smart appliances can be controlled remotely and provide status updates. Intrusion detection systems use sensors to detect intruders and raise alerts. Smoke and gas detectors help detect fires and harmful gases in the home.

no sql ppt.pptx

A graph database stores data as nodes and edges where nodes represent entities and edges represent relationships between those entities. Some popular graph databases include Neo4j, OrientDB, and ArangoDB.
A graph has three main components: nodes which represent objects or instances, relationships which establish connections between nodes, and properties which are data attached to nodes.
Graph databases are useful for applications such as fraud detection, digital asset management, network management, context-aware services, and real-time recommendations. They allow for complex queries of interconnected data.

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This document discusses non-repudiation and Pretty Good Privacy (PGP). It defines non-repudiation as a situation where an author cannot deny authorship or validity of a contract. In digital security, non-repudiation provides proof of integrity, origin and authentication of data. Trusted third parties like forensic analysts and notaries can verify identities and signatures. PGP was invented by Phil Zimmermann and uses digital signatures and encryption to provide privacy, integrity, authentication and non-repudiation for email. It uses hashing, secret keys and public-private key pairs in its operations.

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This document discusses fault tolerance and various techniques used to achieve it. It defines fault tolerance as the ability of a system to continue operating properly even if some of its components fail. It then describes several common techniques: replication runs identical systems in parallel; redundancy uses backups that switch in if one fails; diversity implements the same specifications differently; built-in self-test allows systems to periodically check themselves; triple modular redundancy runs three copies and votes on results; and software techniques include n-version programming, recovery blocks, and checkpointing with rollback recovery. The document emphasizes that fault tolerance is important for critical systems like transportation and the military.

static method,adding method dynamically.pptx

This document discusses static methods and dynamically adding methods in Python. It defines static methods as methods that are not bound to an object and cannot access instance variables. Static methods are defined using the @staticmethod decorator or staticmethod() function. Methods can be dynamically added to classes or objects at runtime. This allows adding methods to built-in types by subclassing them. Classes themselves can also be dynamically created using the type() function, specifying a name, base classes, and attributes.

data structure of symbol table.pptx

This document discusses symbol tables, which are data structures used by compilers to track semantic information about names. Symbol tables allow compilers to determine if a variable is defined, determine scope, and perform search, insert, and delete operations on names. There are four common symbol table structures - unordered lists, ordered lists, tree structures, and hash tables. Hash tables are most commonly used because they provide efficient search, insert and delete operations when memory space is adequately larger than the number of variables. Symbol table entries typically include the name and attributes like type, reserved word, variable name, procedure name, or constant name.

nagavarthini ppt.pptx

This document discusses servlets, which are Java programs that generate dynamic web pages on the server side. It defines servlets and explains that they are used to create robust and scalable web applications. The document outlines the main advantages of servlets over previous technologies like CGI, including that servlets are more efficient through multithreading and avoid starting new processes for each request. It also discusses some of the main concepts involved in servlet programming like the servlet life cycle and client interaction.

filters & security issues.pptx

This document discusses filters and security issues related to filters. It defines filters as objects involved in preprocessing and postprocessing requests. Filters are pluggable and defined in the web.xml file. Advantages of filters include being pluggable and having no dependencies. The document also discusses security issues like sandboxing and permissions for servlets, and security features of the Java Virtual Machine and security APIs.

type of edge detector.pptx

type of edge detector.pptx

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helpline number in women empowerment.pptx

helpline number in women empowerment.pptx

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home automation.pptx

home automation.pptx

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no sql ppt.pptx

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cryptography.pptx

cryptography.pptx

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reliability and fault tolerance.pptx

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static method,adding method dynamically.pptx

static method,adding method dynamically.pptx

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data structure of symbol table.pptx

data structure of symbol table.pptx

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filters & security issues.pptx

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HYPERTENSION - SLIDE SHARE PRESENTATION.

IT WILL BE HELPFULL FOR THE NUSING STUDENTS
IT FOCUSED ON MEDICAL MANAGEMENT AND NURSING MANAGEMENT.
HIGHLIGHTS ON HEALTH EDUCATION.

220711130083 SUBHASHREE RAKSHIT Internet resources for social science

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skeleton System.pdf (skeleton system wow)

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220711130088 Sumi Basak Virtual University EPC 3.pptx

Virtual University

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The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
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Social Laboratory, New Zealand,
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These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.

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๐๐ข๐ฌ๐๐ฎ๐ฌ๐ฌ ๐ญ๐ก๐ ๐๐๐ ๐๐ฎ๐ซ๐ซ๐ข๐๐ฎ๐ฅ๐ฎ๐ฆ ๐ข๐ง ๐ญ๐ก๐ ๐๐ก๐ข๐ฅ๐ข๐ฉ๐ฉ๐ข๐ง๐๐ฌ:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
๐๐ฑ๐ฉ๐ฅ๐๐ข๐ง ๐ญ๐ก๐ ๐๐๐ญ๐ฎ๐ซ๐ ๐๐ง๐ ๐๐๐จ๐ฉ๐ ๐จ๐ ๐๐ง ๐๐ง๐ญ๐ซ๐๐ฉ๐ซ๐๐ง๐๐ฎ๐ซ:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.

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NIPER JEE PYQ
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skeleton System.pdf (skeleton system wow)

skeleton System.pdf (skeleton system wow)

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ุตุญู ุงููุฑุงุกุงุช ุงูุนุดุฑ ุฃุนุฏ ุฃุญุฑู ุงูุฎูุงู ุณู
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ุตุญู ุงููุฑุงุกุงุช ุงูุนุดุฑ ุฃุนุฏ ุฃุญุฑู ุงูุฎูุงู ุณู
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220711130088 Sumi Basak Virtual University EPC 3.pptx

220711130088 Sumi Basak Virtual University EPC 3.pptx

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- 1. NADAR SARASWATHI COLLEGE OF ARTS AND SCIENCE,THENI. DEPARTMENT OF COMPUTER SCIENCE TRAVELING SALESPERSON BY B.POORANI
- 2. TRAVELING SALESPERSON ๏ด Let G=(V,E) be a directed graph defining an instance of the traveling salesperson problem. ๏ด Let Cij be the cost of edges (i,j),Cij=infinity if (i,j) and let V=N. ๏ด Each leaf node L is a solution node and represents the tour defined by the path from the root to L.
- 3. ๏ดThe progress of the LCBB algorithm on the problem instance. ๏ดThe initial reduced matrix is that upper=โ.The portion of the state space tree that gets generated. ๏ดStarting with the root node as the E-node ,nodes 2, 3,4 and 5 are generated.
- 4. ๏ด The matrix of is obtained from the by (1) setting all entries in row 1 and column 3 to โ. ๏ด (2) setting the element at position (3,1) to โ,and (3) reducing column 1 by subtracting by 11. ๏ด The value of upper is unchanged and node 4 becomes the next E-node . Its children 6,7 and 8 are generated. ๏ด The live nodes at this time are node 2,3,5,6,7 and 8 .
- 5. ๏ด Node 6 has least c value and become the next E-node. ๏ด Nodes 9 and 10 are generated. ๏ด Node 10 is the next E-node . The solution node , node 11 , is generated. ๏ด The tour length for this node is c(11)=28 and upper is updated to 28. ๏ด For the next E-node , node 5,c(5)=31>upper.
- 6. ๏ด To use LCBB to search the traveling salesperson state space tree , we need to define a cost function c(.) and two other function c^(.) and u(.). ๏ด c(A)={length of tour defined by the path from the root to A , if A is a leaf .cost of a minimum-cost leaf in the subtree A , if A is not a leaf.
- 7. ๏ด A different LCBB algorithm can be arrived at by considering a different tree organization for the solution space. ๏ด This organization is reached by regarding a tour as a collection of n edges. If G=(V,E) has e edges , then every tour contains exactly n of the e edges. ๏ด A possible organization for the state space is a binary tree in which a left branch represents the inclusion of a particular edges while the right branch represents the exclusion of the edges.
- 8. ๏ด An example of low LCBB would work on the dynamic binary tree formulation . consider the cost matrix . ๏ด We must decide which edges to use to partition the solution space into two subsets . if edges (i,j) is used , then the left subtree of the root represents all tour including edges (i,j) and the right subtree represents all tour that do not include edges (i,j).
- 9. Thank you