This document discusses the impact of technology on humanity through various technological advancements such as television, mobile phones, computers, and robotics. It provides key details on how these technologies have become an integral part of society and the roles they play in people's lives. However, it also notes some ethical dilemmas that have arisen from increased technology use, such as debates around how devices may negatively impact children's health and development or allow uncontrolled access to certain content. The document emphasizes technology's influence on humanity and the responsibility that comes with further innovation.
This document provides an introduction to graph theory, including basic terminology and concepts. It defines what a graph is mathematically as a collection of vertices and edges. It describes different types of graphs such as simple graphs, multiple graphs, weighted graphs, finite and infinite graphs, labeled graphs, and directed graphs. It also defines graph theory terms like adjacency, incidence, degree of a vertex, isomorphism, subgraphs, and graph operations like union and intersection. The document provides examples to illustrate each term and concept.
This document discusses asymptotic notations which are mathematical tools used to analyze the time and space complexity of algorithms. It introduces Big O, Big Omega, and Big Theta notations. Big O notation represents the upper bound and worst case time complexity. Big Omega notation represents the lower bound and best case time complexity. Big Theta notation defines the average time complexity of an algorithm. Examples are provided for how to determine the asymptotic notation of polynomial functions.
This document discusses randomized algorithms. It begins by listing different categories of algorithms, including randomized algorithms. Randomized algorithms introduce randomness into the algorithm to avoid worst-case behavior and find efficient approximate solutions. Quicksort is presented as an example randomized algorithm, where randomness improves its average runtime from quadratic to linear. The document also discusses the randomized closest pair algorithm and a randomized algorithm for primality testing. Both introduce randomness to improve efficiency compared to deterministic algorithms for the same problems.
The document discusses asymptotic notations that are used to describe the time complexity of algorithms. It introduces big O notation, which describes asymptotic upper bounds, big Omega notation for lower bounds, and big Theta notation for tight bounds. Common time complexities are described such as O(1) for constant time, O(log N) for logarithmic time, and O(N^2) for quadratic time. The notations allow analyzing how efficiently algorithms use resources like time and space as the input size increases.
The solution to the single-source shortest-path tree problem in graph theory. This slide was prepared for Design and Analysis of Algorithm Lab for B.Tech CSE 2nd Year 4th Semester.
This presnetation gives complete idea about block diagram representation and reduction techniques to find transfer function. Also gives complete idea about Signal flow graph method to find transfer function.
Algorithms Lecture 2: Analysis of Algorithms IMohamed Loey
This document discusses analysis of algorithms and time complexity. It explains that analysis of algorithms determines the resources needed to execute algorithms. The time complexity of an algorithm quantifies how long it takes. There are three cases to analyze - worst case, average case, and best case. Common notations for time complexity include O(1), O(n), O(n^2), O(log n), and O(n!). The document provides examples of algorithms and determines their time complexity in different cases. It also discusses how to combine complexities of nested loops and loops in algorithms.
This document discusses algorithms and their analysis. It defines an algorithm as a step-by-step procedure to solve a problem or calculate a quantity. Algorithm analysis involves evaluating memory usage and time complexity. Asymptotics, such as Big-O notation, are used to formalize the growth rates of algorithms. Common sorting algorithms like insertion sort and quicksort are analyzed using recurrence relations to determine their time complexities as O(n^2) and O(nlogn), respectively.
We will discuss the following: Graph, Directed vs Undirected Graph, Acyclic vs Cyclic Graph, Backedge, Search vs Traversal, Breadth First Traversal, Depth First Traversal, Detect Cycle in a Directed Graph.
The document discusses several shortest path algorithms for graphs, including Dijkstra's algorithm, Bellman-Ford algorithm, and Floyd-Warshall algorithm. Dijkstra's algorithm finds the shortest path from a single source node to all other nodes in a graph with non-negative edge weights. Bellman-Ford can handle graphs with negative edge weights but is slower. Floyd-Warshall can find shortest paths in a graph between all pairs of nodes.
An algorithm is a finite set of instructions to accomplish a predefined task. Performance of an algorithm is measured by its time and space complexity, with common metrics being big O, big Omega, and big Theta notation. Common data structures include arrays, linked lists, stacks, queues, trees and graphs. Key concepts are asymptotic analysis of algorithms, recursion, and analyzing complexity classes like constant, linear, quadratic and logarithmic time.
This document discusses the complexity of algorithms and the tradeoff between algorithm cost and time. It defines algorithm complexity as a function of input size that measures the time and space used by an algorithm. Different complexity classes are described such as polynomial, sub-linear, and exponential time. Examples are given to find the complexity of bubble sort and linear search algorithms. The concept of space-time tradeoffs is introduced, where using more space can reduce computation time. Genetic algorithms are proposed to efficiently solve large-scale construction time-cost tradeoff problems.
This document summarizes a lecture on algorithms and graph traversal techniques. It discusses:
1) Breadth-first search (BFS) and depth-first search (DFS) algorithms for traversing graphs. BFS uses a queue while DFS uses a stack.
2) Applications of BFS and DFS, including finding connected components, minimum spanning trees, and bi-connected components.
3) Identifying articulation points to determine biconnected components in a graph.
4) The 0/1 knapsack problem and approaches for solving it using greedy algorithms, backtracking, and branch and bound search.
The document discusses the analysis of algorithms. It begins by defining an algorithm and describing different types. It then covers analyzing algorithms in terms of correctness, time efficiency, space efficiency, and optimality through theoretical and empirical analysis. The document discusses analyzing time efficiency by determining the number of repetitions of basic operations as a function of input size. It provides examples of input size, basic operations, and formulas for counting operations. It also covers analyzing best, worst, and average cases and establishes asymptotic efficiency classes. The document then analyzes several examples of non-recursive and recursive algorithms.
It is related to Analysis and Design Of Algorithms Subject.Basically it describe basic of topological sorting, it's algorithm and step by step process to solve the example of topological sort.
This document provides an overview of algorithm analysis. It discusses how to analyze the time efficiency of algorithms by counting the number of operations and expressing efficiency using growth functions. Different common growth rates like constant, linear, quadratic, and exponential are introduced. Examples are provided to demonstrate how to determine the growth rate of different algorithms, including recursive algorithms, by deriving their time complexity functions. The key aspects covered are estimating algorithm runtime, comparing growth rates of algorithms, and using Big O notation to classify algorithms by their asymptotic behavior.
The document discusses the all pairs shortest path problem, which aims to find the shortest distance between every pair of vertices in a graph. It explains that the algorithm works by calculating the minimum cost to traverse between nodes using intermediate nodes, according to the equation A_k(i,j)=min{A_{k-1}(i,j), A_{k-1}(i,k), A_{k-1}(k,j)}. An example is provided to illustrate calculating the shortest path between nodes over multiple iterations of the algorithm.
Algorithms Lecture 3: Analysis of Algorithms IIMohamed Loey
We will discuss the following: Maximum Pairwise Product, Fibonacci, Greatest Common Divisors, Naive algorithm is too slow. The Efficient algorithm is much better. Finding the correct algorithm requires knowing something interesting about the problem
The document discusses recursion, including:
1) Recursion involves breaking a problem down into smaller subproblems until a base case is reached, then building up the solution to the overall problem from the solutions to the subproblems.
2) A recursive function is one that calls itself, with each call typically moving closer to a base case where the problem can be solved without recursion.
3) Recursion can be linear, involving one recursive call, or binary, involving two recursive calls to solve similar subproblems.
The document defines and describes various graph concepts and data structures used to represent graphs. It defines a graph as a collection of nodes and edges, and distinguishes between directed and undirected graphs. It then describes common graph terminology like adjacent/incident nodes, subgraphs, paths, cycles, connected/strongly connected components, trees, and degrees. Finally, it discusses two common ways to represent graphs - the adjacency matrix and adjacency list representations, noting their storage requirements and ability to add/remove nodes.
1. Introduction to time and space complexity.
2. Different types of asymptotic notations and their limit definitions.
3. Growth of functions and types of time complexities.
4. Space and time complexity analysis of various algorithms.
This slides contains assymptotic notations, recurrence relation like subtitution method, iteration method, master method and recursion tree method and sorting algorithms like merge sort, quick sort, heap sort, counting sort, radix sort and bucket sort.
This document discusses recurrences and algorithms analysis. It covers:
1. Recurrences arise when an algorithm contains recursive calls to itself. The running time is described by a recurrence relation.
2. Examples of recurrence relations are given for different types of recursive algorithms.
3. The binary search algorithm is presented as an example recursive algorithm and its recurrence relation is derived.
Skiena algorithm 2007 lecture10 graph data strctureszukun
This document summarizes different types of graph data structures and representations. It discusses graphs as consisting of vertices and edges, and describes properties like directed vs undirected, weighted vs unweighted, sparse vs dense. It also covers different representations like adjacency matrices and lists, and provides code examples for initializing, reading, and inserting edges into a graph represented using an adjacency list.
Algorithms Lecture 2: Analysis of Algorithms IMohamed Loey
This document discusses analysis of algorithms and time complexity. It explains that analysis of algorithms determines the resources needed to execute algorithms. The time complexity of an algorithm quantifies how long it takes. There are three cases to analyze - worst case, average case, and best case. Common notations for time complexity include O(1), O(n), O(n^2), O(log n), and O(n!). The document provides examples of algorithms and determines their time complexity in different cases. It also discusses how to combine complexities of nested loops and loops in algorithms.
This document discusses algorithms and their analysis. It defines an algorithm as a step-by-step procedure to solve a problem or calculate a quantity. Algorithm analysis involves evaluating memory usage and time complexity. Asymptotics, such as Big-O notation, are used to formalize the growth rates of algorithms. Common sorting algorithms like insertion sort and quicksort are analyzed using recurrence relations to determine their time complexities as O(n^2) and O(nlogn), respectively.
We will discuss the following: Graph, Directed vs Undirected Graph, Acyclic vs Cyclic Graph, Backedge, Search vs Traversal, Breadth First Traversal, Depth First Traversal, Detect Cycle in a Directed Graph.
The document discusses several shortest path algorithms for graphs, including Dijkstra's algorithm, Bellman-Ford algorithm, and Floyd-Warshall algorithm. Dijkstra's algorithm finds the shortest path from a single source node to all other nodes in a graph with non-negative edge weights. Bellman-Ford can handle graphs with negative edge weights but is slower. Floyd-Warshall can find shortest paths in a graph between all pairs of nodes.
An algorithm is a finite set of instructions to accomplish a predefined task. Performance of an algorithm is measured by its time and space complexity, with common metrics being big O, big Omega, and big Theta notation. Common data structures include arrays, linked lists, stacks, queues, trees and graphs. Key concepts are asymptotic analysis of algorithms, recursion, and analyzing complexity classes like constant, linear, quadratic and logarithmic time.
This document discusses the complexity of algorithms and the tradeoff between algorithm cost and time. It defines algorithm complexity as a function of input size that measures the time and space used by an algorithm. Different complexity classes are described such as polynomial, sub-linear, and exponential time. Examples are given to find the complexity of bubble sort and linear search algorithms. The concept of space-time tradeoffs is introduced, where using more space can reduce computation time. Genetic algorithms are proposed to efficiently solve large-scale construction time-cost tradeoff problems.
This document summarizes a lecture on algorithms and graph traversal techniques. It discusses:
1) Breadth-first search (BFS) and depth-first search (DFS) algorithms for traversing graphs. BFS uses a queue while DFS uses a stack.
2) Applications of BFS and DFS, including finding connected components, minimum spanning trees, and bi-connected components.
3) Identifying articulation points to determine biconnected components in a graph.
4) The 0/1 knapsack problem and approaches for solving it using greedy algorithms, backtracking, and branch and bound search.
The document discusses the analysis of algorithms. It begins by defining an algorithm and describing different types. It then covers analyzing algorithms in terms of correctness, time efficiency, space efficiency, and optimality through theoretical and empirical analysis. The document discusses analyzing time efficiency by determining the number of repetitions of basic operations as a function of input size. It provides examples of input size, basic operations, and formulas for counting operations. It also covers analyzing best, worst, and average cases and establishes asymptotic efficiency classes. The document then analyzes several examples of non-recursive and recursive algorithms.
It is related to Analysis and Design Of Algorithms Subject.Basically it describe basic of topological sorting, it's algorithm and step by step process to solve the example of topological sort.
This document provides an overview of algorithm analysis. It discusses how to analyze the time efficiency of algorithms by counting the number of operations and expressing efficiency using growth functions. Different common growth rates like constant, linear, quadratic, and exponential are introduced. Examples are provided to demonstrate how to determine the growth rate of different algorithms, including recursive algorithms, by deriving their time complexity functions. The key aspects covered are estimating algorithm runtime, comparing growth rates of algorithms, and using Big O notation to classify algorithms by their asymptotic behavior.
The document discusses the all pairs shortest path problem, which aims to find the shortest distance between every pair of vertices in a graph. It explains that the algorithm works by calculating the minimum cost to traverse between nodes using intermediate nodes, according to the equation A_k(i,j)=min{A_{k-1}(i,j), A_{k-1}(i,k), A_{k-1}(k,j)}. An example is provided to illustrate calculating the shortest path between nodes over multiple iterations of the algorithm.
Algorithms Lecture 3: Analysis of Algorithms IIMohamed Loey
We will discuss the following: Maximum Pairwise Product, Fibonacci, Greatest Common Divisors, Naive algorithm is too slow. The Efficient algorithm is much better. Finding the correct algorithm requires knowing something interesting about the problem
The document discusses recursion, including:
1) Recursion involves breaking a problem down into smaller subproblems until a base case is reached, then building up the solution to the overall problem from the solutions to the subproblems.
2) A recursive function is one that calls itself, with each call typically moving closer to a base case where the problem can be solved without recursion.
3) Recursion can be linear, involving one recursive call, or binary, involving two recursive calls to solve similar subproblems.
The document defines and describes various graph concepts and data structures used to represent graphs. It defines a graph as a collection of nodes and edges, and distinguishes between directed and undirected graphs. It then describes common graph terminology like adjacent/incident nodes, subgraphs, paths, cycles, connected/strongly connected components, trees, and degrees. Finally, it discusses two common ways to represent graphs - the adjacency matrix and adjacency list representations, noting their storage requirements and ability to add/remove nodes.
1. Introduction to time and space complexity.
2. Different types of asymptotic notations and their limit definitions.
3. Growth of functions and types of time complexities.
4. Space and time complexity analysis of various algorithms.
This slides contains assymptotic notations, recurrence relation like subtitution method, iteration method, master method and recursion tree method and sorting algorithms like merge sort, quick sort, heap sort, counting sort, radix sort and bucket sort.
This document discusses recurrences and algorithms analysis. It covers:
1. Recurrences arise when an algorithm contains recursive calls to itself. The running time is described by a recurrence relation.
2. Examples of recurrence relations are given for different types of recursive algorithms.
3. The binary search algorithm is presented as an example recursive algorithm and its recurrence relation is derived.
Skiena algorithm 2007 lecture10 graph data strctureszukun
This document summarizes different types of graph data structures and representations. It discusses graphs as consisting of vertices and edges, and describes properties like directed vs undirected, weighted vs unweighted, sparse vs dense. It also covers different representations like adjacency matrices and lists, and provides code examples for initializing, reading, and inserting edges into a graph represented using an adjacency list.
Mathematics (from Greek μάθημα máthēma, “knowledge, study, learning”) is the study of topics such as quantity (numbers), structure, space, and change. There is a range of views among mathematicians and philosophers as to the exact scope and definition of mathematics
This chapter discusses different types of graphs including undirected graphs, directed graphs, and weighted graphs. It describes common graph terminology like vertices, edges, paths, cycles, and trees. It also covers common graph algorithms like breadth-first search, depth-first search, minimum spanning trees, and shortest paths. Finally, it discusses strategies for implementing graphs using adjacency lists and adjacency matrices.
Basic Mathematics (PPISMP) - representing data into bar graphT-ah Atirah
The document discusses representing income data from a store in Kuala Terengganu, Malaysia from 2003-2005 using bar graphs. It provides the store's annual total revenues, expenses, and profits. A vertical bar graph, horizontal bar graph, and pie chart could be used. Key information that can be gleaned from the graphs is the highest and lowest revenues, expenses, and profits by year, and differences in amounts between years. Total profits were RM70,150 over the three years, with the highest in 2004 at RM26,101 and lowest in 2005 at RM18,402.
1. The document discusses depth-first search (DFS), including its recursive implementation which eliminates the need for an explicit stack. 2. DFS classifies the edges of a graph as tree edges, back edges, forward edges, or cross edges. Tree edges and back edges are the only possible edge types in an undirected graph searched with DFS. 3. Applications of DFS include finding cycles, articulation vertices, topological sorting of directed acyclic graphs, and finding strongly connected components.
Simple algorithm & hopcroft karp for bipartite graphMiguel Pereira
The document discusses algorithms for maximum matchings in bipartite graphs. It defines bipartite graphs and matchings. A simple algorithm finds augmenting paths and increments the matching size in each iteration, having complexity O(VE). The more efficient Hopcroft-Karp algorithm finds a maximal set of shortest augmenting paths and augments along all paths simultaneously in each iteration, achieving complexity O(√V√E). It demonstrates the algorithm augmenting the matching by alternating along paths in the maximal set.
Graph Traversal Algorithms - Breadth First SearchAmrinder Arora
The document discusses branch and bound algorithms. It begins with an overview of breadth first search (BFS) and how it can be used to solve problems on infinite mazes or graphs. It then provides pseudocode for implementing BFS using a queue data structure. Finally, it discusses branch and bound as a general technique for solving optimization problems that applies when greedy methods and dynamic programming fail. Branch and bound performs a BFS-like search, but prunes parts of the search tree using lower and upper bounds to avoid exploring all possible solutions.
Graph Traversal Algorithms - Depth First Search TraversalAmrinder Arora
This document discusses graph traversal techniques, specifically depth-first search (DFS) and breadth-first search (BFS). It provides pseudocode for DFS and explains key properties like edge classification, time complexity of O(V+E), and applications such as finding connected components and articulation points.
The document discusses different matrix representations of graphs:
1) Incidence matrix shows which edges are incident to each vertex with 1s and 0s.
2) Adjacency matrix shows which vertices are adjacent to each other with 1s and 0s.
3) Cut-set matrix shows which edges are part of given cut sets that disconnect the graph with 1s and 0s.
A graph consists of vertices and edges, where vertices represent entities and edges represent relationships between vertices. Graphs can be represented sequentially using matrices like adjacency and incidence matrices, or linked using data structures like adjacency lists. Adjacency matrices allow fast addition/removal of edges but use more memory, while adjacency lists use less memory but are slower to modify. The best representation depends on whether the graph is dense or sparse.
The document defines topological sorting as a linear ordering of the vertices in a directed acyclic graph (DAG) such that if there is a path from vertex vi to vk, then vi appears before vk in the ordering. It provides an algorithm for finding a topological sort by initializing an array, finding vertices of degree zero, adding them to the array, and removing outgoing edges. It also describes an algorithm for enumerating all possible topological sorts by computing indegree counts and pushing vertices with indegree of zero onto a stack.
This document provides definitions and theorems related to graph theory. It begins with definitions of simple graphs, vertices, edges, degree, and the handshaking lemma. It then covers definitions and properties of paths, cycles, adjacency matrices, connectedness, Euler paths and circuits. The document also discusses Hamilton paths, planar graphs, trees, and other special types of graphs like complete graphs and bipartite graphs. It provides examples and proofs of many graph theory concepts and results.
The document discusses different types of graphs and paths in graph theory:
- It defines a graph as a set of vertices connected by edges, and defines directed and simple graphs. It provides an example of each.
- It discusses terminology related to graphs like adjacent vertices, endpoints of edges, and subgraphs.
- It defines the degree of a vertex and total degree of a graph. It provides an example calculation of vertex degree.
- It defines different types of paths in graphs like simple paths, circuits, and simple circuits. It provides examples of each.
- It defines Eulerian circuits and paths as paths or circuits that pass through each edge exactly once. It provides examples of graphs that do
This document section summarizes information about Euler paths and circuits as well as Hamilton paths and circuits. It discusses:
- Euler's solving of the Königsberg bridge problem and defining Euler paths/circuits
- Necessary and sufficient conditions for a graph to have an Euler path/circuit
- Algorithms for constructing Euler circuits
- Applications of Euler paths/circuits
- Defining Hamilton paths/circuits and discussing properties like Dirac's and Ore's theorems for Hamilton circuits
- Differences between Euler and Hamilton circuits
This document discusses several graph theory concepts including:
- A connected undirected graph contains a simple path between any two vertices.
- Cut vertices and edges divide a graph into more components upon removal.
- Euler circuits and paths visit every edge exactly once in a connected multigraph.
- Planar graphs can be drawn in a plane without edge crossings.
- Graph coloring assigns colors to vertices such that no adjacent vertices share a color.
Graph theory is the study of mathematical structures called graphs that are used to model pairwise relations between objects. Key concepts include vertices, edges, degrees of vertices, walks, paths, circuits, subgraphs, complete graphs, planar graphs, Eulerian graphs, and Hamiltonian graphs. Graph theory has many applications including modeling networks, optimization problems, and molecular structures. The origins of graph theory can be traced back to Euler's solution to the Königsberg bridge problem in 1736.
Graph theory is the study of mathematical structures called graphs that are used to model pairwise relations between objects. Key concepts include vertices, edges, degrees, walks, paths, circuits, trees, planar graphs and non-planar graphs. Graph theory originated from Euler's solution to the Königsberg bridge problem in 1736. Important applications of graph theory include modeling networks, optimization problems, and molecular structures.
The document discusses graphs and networks, including routing problems, routes and paths, connected graphs, weighted graphs, spanning trees, and minimal spanning trees. It provides examples and definitions of key concepts like vertices, edges, paths, circuits, connected graphs, weighted graphs, spanning trees, and minimal spanning trees. Kruskal's algorithm for finding a minimal spanning tree is also explained.
The document discusses graphs and circuits on graphs. It defines key graph terminology like vertices, edges, walks, paths, circuits, connectedness, subgraphs, and Euler and Hamiltonian circuits. It presents theorems characterizing when a graph has an Euler circuit or Euler trail. For example, a graph has an Euler circuit if and only if it is connected and every vertex has even degree. It also provides examples and algorithms for determining if graphs have certain circuits.
The document discusses graph isomorphism, which is determining if two graphs can be drawn in the same way. Two graphs are isomorphic if there is a one-to-one correspondence between their vertices that preserves adjacency. The document also provides examples of isomorphic and non-isomorphic graphs. It further discusses concepts like paths, connectedness, subgraphs, cut-sets, Euler paths and circuits, and Hamilton paths and circuits. Theorems related to conditions for the existence of Eulerian and Hamiltonian graphs are also presented.
A connected multigraph has an Euler circuit if and only if every vertex has an even degree. A proof is provided that shows every time the circuit passes through a vertex, it contributes two to the vertex's degree, so all degrees must be even. It is also shown that if every vertex has an even degree, an Euler circuit can be constructed by building a path and splicing subpaths together.
A connected multigraph has an Euler path but not a circuit if and only if it has exactly two vertices of odd degree. Adding an edge between these two vertices would create an Euler circuit.
Graph theory is the study of graphs, which are mathematical structures used to model pairwise relations between objects. A graph consists of vertices and edges connecting pairs of vertices. There are many types of graphs including trees, which are connected acyclic graphs. Spanning trees are subgraphs of a graph that connect all vertices using the minimum number of edges. Key concepts in graph theory include paths, connectedness, cycles, and isomorphism between graphs.
The document defines key concepts in graph theory including:
- Types of graphs such as simple graphs, connected graphs, and regular graphs.
- Graph terminology like vertices, edges, walks, paths, and subgraphs.
- Special graphs like Hamiltonian and Euler graphs.
- Graph coloring problems including vertex coloring and edge coloring.
- Examples are given to illustrate graph concepts and properties.
This document provides an introduction to graphs and graph terminology. It defines what a graph is composed of, including vertices and edges. It gives examples of graphs and has the student practice identifying vertices and edges. It also introduces common graph terminology like degree of a vertex, adjacent vertices, paths, circuits, bridges, Euler paths, and Euler circuits. Fleury's algorithm for finding an Euler circuit or path in a graph is described. The document uses examples and exercises to help students learn and practice applying these graph concepts.
GRAPH THEORY - Basic definition with examplesGayathri M
In this presentation, I have explained all the fundamentals of graph theory, from its definition to its types, and various ways to represent graphs as well.
The document discusses graphs and Eulerian circuits and paths. It defines what a graph is composed of and defines an Eulerian circuit and path. It states that a graph must be connected and have all vertices visited once for an Eulerian circuit, or have two odd vertices for an Eulerian path. Fleury's algorithm is described for finding an Euler circuit or path by traversing edges without crossing bridges twice. The algorithm works by choosing the next edge such that bridges are only crossed if necessary. Examples are given to demonstrate the algorithm. Applications mentioned include the Chinese postman problem and communicating networks.
This document provides information about graphs and graph theory concepts. It defines what a graph is consisting of vertices and edges. It describes different types of graphs such as undirected graphs, directed graphs, multigraphs, and pseudographs. It also discusses graph representations using adjacency matrices, adjacency lists, and incidence matrices. Additionally, it covers graph properties and concepts such as degrees of vertices, connected graphs, connected components, planar graphs, graph coloring, and the five color theorem.
This document provides an introduction to graph theory. It defines a graph as consisting of vertices and edges, and defines terms like adjacent vertices and incident edges. It discusses different types of graphs like subgraphs, isomorphic graphs, and homeomorphic graphs. It describes two common ways to represent graphs in memory: adjacency matrices and linked representations using adjacency lists. It also covers concepts like traversable graphs, Euler circuits that use every edge exactly once, and Hamiltonian circuits that visit each vertex exactly once.
Euler proved that it is impossible to traverse all the bridges in Königsberg once each by inventing the mathematical graph. He showed that the Königsberg bridge network has 4 vertices, all with an odd number of edges, meaning it is not possible to start and end at the same location using each bridge only once. Adding an 8th bridge would make it possible by creating only 2 odd vertices. A graph is traversable if it has 0 or 2 vertices with an odd number of edges.
Graph theory is the study of graphs, which are mathematical structures used to model pairwise relationships between objects. A graph consists of vertices and edges that connect the vertices. Graph theory is used in computer science to model problems that can be represented as networks, such as determining routes in a city or designing circuits. It allows analyzing properties of graphs like connectivity and determining the minimum number of resources required. A graph can be represented using an adjacency matrix or adjacency list to store information about its vertices and edges.
This document provides a summary of Md.Delwar Hossain's background and experience. He has a B.Sc in Computer Science and Engineering and M.Sc in Computer Science from Jahangirnagar University. He currently works as an instructor and IT trainer with experience training at Open IT and working as a computer operator. He has professional certifications in BASIS, EATL, APPSO, and Google Android.
The document discusses an introduction to mobile platforms and Android app development. It provides an overview of mobile device types, different mobile platforms, and trends in mobile applications. It then focuses on Android, describing it as an open source operating system and development platform. Key features of the Android SDK are outlined, including access to hardware, multimedia capabilities, and support for network technologies. Resources for app development are also mentioned.
The document discusses various topics related to distributed database management systems (DDBMS), including:
1) Transparency in DDBMS, which aims to hide the complexities of distribution from users. There are four main categories: distribution, transaction, performance, and DBMS transparency.
2) Distributed transaction management, which must ensure the ACID properties of transactions that access data across multiple sites. Objectives include improving utilization, minimizing response time and communication cost.
3) Concurrency control in DDBMS, which coordinates concurrent access to distributed data. Locking-based protocols are commonly used, where transactions acquire read or write locks before accessing data items. The two-phase locking protocol is also discussed.
This document outlines different database system architectures, including centralized systems, client-server systems, transaction server systems, data server systems, parallel processing systems, and distributed database systems. Centralized systems are run on a single computer, while distributed database systems consist of multiple logically related databases distributed over a computer network and managed through a distributed database management system. Parallel processing systems improve performance through speedup and scaleup using multiple CPUs working concurrently.
The document discusses functional dependency and normalization. It defines functional dependency and outlines Armstrong's axioms for functional dependencies. It also defines normalization objectives and normal forms including 1NF, 2NF and 3NF. The document provides examples of functional dependencies and canonical covers. It discusses anomalies that can occur in 1NF relations including insertion, deletion and update anomalies. Finally, it defines partial and transitive dependencies.
This document provides an overview of indexing and hashing techniques for database systems. It discusses ordered indices like primary and secondary indices, which are based on sorted keys. It also covers hash indices, which distribute keys uniformly across hash buckets. The document evaluates different indexing techniques based on factors like access time, insertion/deletion time, and space overhead. It describes B+ tree indices, which maintain efficiency during data modifications. Multi-level indexing is introduced to handle large index files that do not fit in memory.
This document discusses various techniques for data recovery in database systems. It begins by classifying types of system failures that can occur such as transaction failures, system crashes, and disk failures. It then describes different storage types and how to implement stable storage using techniques like RAID and remote backups. The document outlines several RAID levels that provide redundancy and improve reliability and performance. It also covers log-based and deferred modification recovery techniques used to recover data after failures and ensure transaction durability and atomicity.
The document discusses distributed query processing. It begins by defining what a query and query processor are. It then describes the main functions, problems, and characteristics of a query processor. The document outlines the main layers of query processing as query decomposition, data localization, global query optimization, and distributed execution. It provides details on each of these layers and processes involved like normalization, analysis, and optimization.
MLDM provides an original scientific position in Europe on problems related to pattern recognition, machine learning, classification, modelling, knowledge extraction and data mining. These issues have a strong employability potential for students trained in the field of modelling, prediction or decision support, as well as in the area of the Web, image and video processing, health informatics, etc.
For graphs of mathematical functions, see Graph of a function. For other uses, see Graph (disambiguation). A drawing of a graph. In mathematics graph theory is the study of graphs, which are mathematical structures used.In mathematics, and more specifically in graph theory, a tree is an undirected graph in which any two vertices are connected by exactly one path. In other words, any acyclic connected graph is a tree. A forest is a disjoint union of trees.
Synchronous Optical Networking (SONET) and Synchronous Digital Hierarchy (SDH) are standardized multiplexing protocols that transfer multiple digital bit streams over optical fiber using lasers or light-emitting diodes (LEDs). Lower data rates can also be transferred via an electrical interface.
Discrete Mathematics - Sets. ... He had defined a set as a collection of definite and distinguishable objects selected by the means of certain rules or description. Set theory forms the basis of several other fields of study like counting theory, relations, graph theory and finite state machines.
Discrete Mathematics - Sets. ... He had defined a set as a collection of definite and distinguishable objects selected by the means of certain rules or description. Set theory forms the basis of several other fields of study like counting theory, relations, graph theory and finite state machines.
The Parallel RLC Circuit is the exact opposite to the series circuit we looked at in the previous tutorial although some of the previous concepts and equations still apply.
The Parallel RLC Circuit is the exact opposite to the series circuit we looked at in the previous tutorial although some of the previous concepts and equations still apply.
Discrete Mathematics - Relations. ... Relations may exist between objects of the same set or between objects of two or more sets. Definition and Properties. A binary relation R from set x to y (written as x R y o r R ( x , y ) ) is a subset of the Cartesian product x × y .
Propositional calculus (also called propositional logic, sentential calculus, sentential logic, or sometimes zeroth-order logic) is the branch of logic concerned with the study of propositions (whether they are true or false) that are formed by other propositions with the use of logical connectives, and how their value depends on the truth value of their components. Logical connectives are found in natural languages.
Propositional calculus (also called propositional logic, sentential calculus, sentential logic, or sometimes zeroth-order logic) is the branch of logic concerned with the study of propositions (whether they are true or false) that are formed by other propositions with the use of logical connectives, and how their value depends on the truth value of their components. Logical connectives are found in natural languages.
Prim's algorithm finds a minimum spanning tree of a connected weighted graph. It starts with a minimum weight edge and adds the minimum weight edge incident to the growing tree at each step, as long as it does not form a cycle. The algorithm is demonstrated on a sample weighted graph, finding a minimum spanning tree of weight 10 using Prim's algorithm and alphabetical order to break ties.
This presentation was provided by Bill Kasdorf of Kasdorf & Associates LLC and Publishing Technology Partners, during the fifth session of the NISO training series "Accessibility Essentials." Session Five: A Standards Seminar, was held May 1, 2025.
APM event hosted by the Midlands Network on 30 April 2025.
Speaker: Sacha Hind, Senior Programme Manager, Network Rail
With fierce competition in today’s job market, candidates need a lot more than a good CV and interview skills to stand out from the crowd.
Based on her own experience of progressing to a senior project role and leading a team of 35 project professionals, Sacha shared not just how to land that dream role, but how to be successful in it and most importantly, how to enjoy it!
Sacha included her top tips for aspiring leaders – the things you really need to know but people rarely tell you!
We also celebrated our Midlands Regional Network Awards 2025, and presenting the award for Midlands Student of the Year 2025.
This session provided the opportunity for personal reflection on areas attendees are currently focussing on in order to be successful versus what really makes a difference.
Sacha answered some common questions about what it takes to thrive at a senior level in a fast-paced project environment: Do I need a degree? How do I balance work with family and life outside of work? How do I get leadership experience before I become a line manager?
The session was full of practical takeaways and the audience also had the opportunity to get their questions answered on the evening with a live Q&A session.
Attendees hopefully came away feeling more confident, motivated and empowered to progress their careers
The Pala kings were people-protectors. In fact, Gopal was elected to the throne only to end Matsya Nyaya. Bhagalpur Abhiledh states that Dharmapala imposed only fair taxes on the people. Rampala abolished the unjust taxes imposed by Bhima. The Pala rulers were lovers of learning. Vikramshila University was established by Dharmapala. He opened 50 other learning centers. A famous Buddhist scholar named Haribhadra was to be present in his court. Devpala appointed another Buddhist scholar named Veerdeva as the vice president of Nalanda Vihar. Among other scholars of this period, Sandhyakar Nandi, Chakrapani Dutta and Vajradatta are especially famous. Sandhyakar Nandi wrote the famous poem of this period 'Ramcharit'.
How to Set warnings for invoicing specific customers in odooCeline George
Odoo 16 offers a powerful platform for managing sales documents and invoicing efficiently. One of its standout features is the ability to set warnings and block messages for specific customers during the invoicing process.
How to manage Multiple Warehouses for multiple floors in odoo point of saleCeline George
The need for multiple warehouses and effective inventory management is crucial for companies aiming to optimize their operations, enhance customer satisfaction, and maintain a competitive edge.
Link your Lead Opportunities into Spreadsheet using odoo CRMCeline George
In Odoo 17 CRM, linking leads and opportunities to a spreadsheet can be done by exporting data or using Odoo’s built-in spreadsheet integration. To export, navigate to the CRM app, filter and select the relevant records, and then export the data in formats like CSV or XLSX, which can be opened in external spreadsheet tools such as Excel or Google Sheets.
A measles outbreak originating in West Texas has been linked to confirmed cases in New Mexico, with additional cases reported in Oklahoma and Kansas. The current case count is 795 from Texas, New Mexico, Oklahoma, and Kansas. 95 individuals have required hospitalization, and 3 deaths, 2 children in Texas and one adult in New Mexico. These fatalities mark the first measles-related deaths in the United States since 2015 and the first pediatric measles death since 2003.
The YSPH Virtual Medical Operations Center Briefs (VMOC) were created as a service-learning project by faculty and graduate students at the Yale School of Public Health in response to the 2010 Haiti Earthquake. Each year, the VMOC Briefs are produced by students enrolled in Environmental Health Science Course 581 - Public Health Emergencies: Disaster Planning and Response. These briefs compile diverse information sources – including status reports, maps, news articles, and web content– into a single, easily digestible document that can be widely shared and used interactively. Key features of this report include:
- Comprehensive Overview: Provides situation updates, maps, relevant news, and web resources.
- Accessibility: Designed for easy reading, wide distribution, and interactive use.
- Collaboration: The “unlocked" format enables other responders to share, copy, and adapt seamlessly. The students learn by doing, quickly discovering how and where to find critical information and presenting it in an easily understood manner.
Title: A Quick and Illustrated Guide to APA Style Referencing (7th Edition)
This visual and beginner-friendly guide simplifies the APA referencing style (7th edition) for academic writing. Designed especially for commerce students and research beginners, it includes:
✅ Real examples from original research papers
✅ Color-coded diagrams for clarity
✅ Key rules for in-text citation and reference list formatting
✅ Free citation tools like Mendeley & Zotero explained
Whether you're writing a college assignment, dissertation, or academic article, this guide will help you cite your sources correctly, confidently, and consistent.
Created by: Prof. Ishika Ghosh,
Faculty.
📩 For queries or feedback: [email protected]
Geography Sem II Unit 1C Correlation of Geography with other school subjectsProfDrShaikhImran
The correlation of school subjects refers to the interconnectedness and mutual reinforcement between different academic disciplines. This concept highlights how knowledge and skills in one subject can support, enhance, or overlap with learning in another. Recognizing these correlations helps in creating a more holistic and meaningful educational experience.
2. Path
• Informally, a path is a sequence of edges that
begins at a vertex of a graph and travels from
vertex to vertex along edges of the graph.
3. Example
• In the simple graph shown in Figure 1, a,
d, c, f , e is a simple path of length 4,
because {a, d},{d, c}, {c, f }, and {f, e} are
all edges. However, d, e, c, a is not a
path, because {e, c} is not an edge.
• Note that b, c, f , e, b is a circuit of
length 4 because {b, c}, {c, f }, {f, e}, and
{e, b} are edges, and this path begins and
ends at b.
• The path a, b, e, d, a, b, which is of
length 5, is not simple because it
contains the edge {a, b} twice.
4. Euler circuit
1.Can we travel along the edges of a graph
starting at a vertex and returning to it by
traversing each edge of the graph exactly
once?
• This question, which asks whether a graph has
an Euler circuit.
5. Definition 1
• An Euler circuit in a graph G is a simple circuit
containing every edge of G. An Euler path in G
is a simple path containing every edge of G.
6. Example1
• The graph G1 has an Euler circuit, for example, a, e, c, d, e, b, a. Neither of
the graphs G2 or G3 has an Euler circuit (the reader should verify this).
However, G3 has an Euler path, namely, a, c, d, e, b, d, a, b. G2 does not
have an Euler path
7. Theorem 2
• A connected multigraph has an Euler path but
not an Euler circuit if and only if it has exactly
two vertices of odd degree.
8. Example2
• G1 contains exactly two vertices of odd degree, namely, b and d. Hence, it
has an Euler path that must have b and d as its endpoints. One such Euler
path is d, a, b, c, d, b. Similarly,G2 has exactly two vertices of odd degree,
namely, b and d. So it has an Euler path that must have b and d as
endpoints. One such Euler path is b, a, g, f, e, d, c, g, b, c, f, d. G3 has no
Euler path because it has six vertices of odd degree.
9. Hamilton circuit
2.Similarly, can we travel along the edges of a
graph starting at a vertex and returning to it
while visiting each vertex of the graph exactly
once?
This question, which asks whether a graph has
a Hamilton circuit
10. Which of the simple graphs in Figure 10 have a Hamilton circuit
or, if not, a Hamilton path?
11. Solution
• G1 has a Hamilton circuit: a, b, c, d, e, a.
• There is no Hamilton circuit in G2 (this can be seen by noting that any
circuit containing every vertex must contain the edge {a, b} twice),
but G2 does have a Hamilton path, namely, a, b, c, d.
• G3 has neither a Hamilton circuit nor a Hamilton path, because any path
containing all vertices must contain one of the edges {a, b},{e, f }, and {c,
d} more than once.
12. What is the length of a shortest path between a and z in the
weighted graph shown in Figure 3?
13. A shortest-path algorithm
• Due to Edsger W. Dijkstra, Dutch
computer scientist born in 1930
• Dijkstra's algorithm finds the length of the
shortest path from a single vertex to any
other vertex in a connected weighted
graph.
16. Hasse diagram
If we want to make a hasse diagram of a relation it must be
Reflexive,Transitive and Anti symmetric
To make a hasse diagram we have to do these:
•omit all loops,
•omit all arrows that can be inferred from transitivity,
•draw arrows without heads,
•understand that all arrows point upwards