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Divide And Conquer Technique
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Tissues are groups of cells that work together to perform specific functions. They are made up of clusters of cells known as tissues. There are different types of tissues in plants and animals since they have different structures and functions. Plants contain three main tissue types - dermal, permanent and meristematic tissues. The four primary animal tissue types are epithelial, connective, muscle and nervous tissues which carry out specialized roles.
3.1 Trees ( Introduction, Binary Trees & Binary Search Trees)
3.1 Trees ( Introduction, Binary Trees & Binary Search Trees)P. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
This document provides information and resources about trees as a data structure for an online class on data structures. It includes links to YouTube videos that explain trees in an animated way and binary trees. It also includes a link to a quiz on Nearpod and discusses binary search trees. The document is from Dr. P. Subathra, a professor in the Department of Information Technology at KAMARAJ College of Engineering & Technology in Madurai, Tamil Nadu, India.2.1 STACK & QUEUE ADTS
This document discusses data structures stacks and queues. It provides definitions and examples of stacks and queues. Stacks follow LIFO (last in first out) and are useful for undo sequences and function calls. Queues follow FIFO (first in first out) and are useful for things like printer queues. The document discusses implementations of stacks and queues using arrays and linked lists. It provides pseudocode for common stack and queue operations like push, pop, enqueue, dequeue.
2.2 stack applications Infix to Postfix & Evaluation of Post Fix
2.2 stack applications Infix to Postfix & Evaluation of Post FixP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
This document discusses applications of stacks, including converting between infix, prefix, and postfix notation. It provides examples of evaluating arithmetic expressions in postfix notation. Key points include:
- Prefix notation places the operator before operands, postfix places after, and infix between.
- Converting infix to prefix moves operators left and removes parentheses. Converting to postfix moves operators to output list in order of evaluation.
- Postfix notation avoids needing parentheses and is evaluated by pushing operands, popping to apply operators, and pushing results back onto the stack.1. 6 doubly linked list
This document discusses doubly linked lists. It begins by explaining some of the limitations of circular singly linked lists, such as the inability to easily traverse backwards. Doubly linked lists are then introduced as a solution, with each node containing pointers to both the next and previous nodes. The key operations on doubly linked lists like insertion at the beginning, end, and middle as well as deletion at the beginning, end, and middle are described algorithmically. Traversal in both the forward and backward directions is also covered. Code examples are provided to demonstrate creating and manipulating doubly linked lists.
1. 5 Circular singly linked list
1. 5 Circular singly linked listP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
The document discusses circular singly linked lists. It begins by explaining the issue with singly linked lists, which is that nodes cannot be revisited. It then introduces circular singly linked lists as a solution. It describes the memory representation of a circular linked list using node pointers. It lists common operations on circular linked lists like insertion, deletion, searching and display. It provides examples of creating a new list, inserting nodes at the beginning and end, and deleting nodes from the beginning and end. It notes that a tail pointer should be used instead of the head pointer for certain operations.1. 4 Singly linked list deletion
1. 4 Singly linked list deletionP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
The document discusses various deletion operations on singly linked lists. It provides algorithms and code snippets for deleting the first element, last element, a given element, and an element at a given position in a singly linked list. The algorithms handle both empty lists and non-empty lists with single and multiple nodes. Key steps include checking for empty lists, traversing the list to find the required element or position while tracking the previous node, and updating pointers after deleting the node.1. 3 singly linked list insertion 2
1. 3 singly linked list insertion 2P. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
This document discusses algorithms for inserting nodes into singly linked lists at different positions. It provides pseudocode and illustrations for inserting a node after a given element, before a given element, and at a specified position.
For each insertion operation, it outlines the steps: 1) create a new node, 2) get the data, 3) check for empty list or specified position and traverse the list to find the insertion point. It then shows how to insert the new node by adjusting the next pointers of the preceding and current nodes. Examples are given with initial and final list illustrations for each insertion type.Recommended
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Tissues are groups of cells that work together to perform specific functions. They are made up of clusters of cells known as tissues. There are different types of tissues in plants and animals since they have different structures and functions. Plants contain three main tissue types - dermal, permanent and meristematic tissues. The four primary animal tissue types are epithelial, connective, muscle and nervous tissues which carry out specialized roles.
3.1 Trees ( Introduction, Binary Trees & Binary Search Trees)
3.1 Trees ( Introduction, Binary Trees & Binary Search Trees)P. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
This document provides information and resources about trees as a data structure for an online class on data structures. It includes links to YouTube videos that explain trees in an animated way and binary trees. It also includes a link to a quiz on Nearpod and discusses binary search trees. The document is from Dr. P. Subathra, a professor in the Department of Information Technology at KAMARAJ College of Engineering & Technology in Madurai, Tamil Nadu, India.2.1 STACK & QUEUE ADTS
This document discusses data structures stacks and queues. It provides definitions and examples of stacks and queues. Stacks follow LIFO (last in first out) and are useful for undo sequences and function calls. Queues follow FIFO (first in first out) and are useful for things like printer queues. The document discusses implementations of stacks and queues using arrays and linked lists. It provides pseudocode for common stack and queue operations like push, pop, enqueue, dequeue.
2.2 stack applications Infix to Postfix & Evaluation of Post Fix
2.2 stack applications Infix to Postfix & Evaluation of Post FixP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
This document discusses applications of stacks, including converting between infix, prefix, and postfix notation. It provides examples of evaluating arithmetic expressions in postfix notation. Key points include:
- Prefix notation places the operator before operands, postfix places after, and infix between.
- Converting infix to prefix moves operators left and removes parentheses. Converting to postfix moves operators to output list in order of evaluation.
- Postfix notation avoids needing parentheses and is evaluated by pushing operands, popping to apply operators, and pushing results back onto the stack.1. 6 doubly linked list
This document discusses doubly linked lists. It begins by explaining some of the limitations of circular singly linked lists, such as the inability to easily traverse backwards. Doubly linked lists are then introduced as a solution, with each node containing pointers to both the next and previous nodes. The key operations on doubly linked lists like insertion at the beginning, end, and middle as well as deletion at the beginning, end, and middle are described algorithmically. Traversal in both the forward and backward directions is also covered. Code examples are provided to demonstrate creating and manipulating doubly linked lists.
1. 5 Circular singly linked list
1. 5 Circular singly linked listP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
The document discusses circular singly linked lists. It begins by explaining the issue with singly linked lists, which is that nodes cannot be revisited. It then introduces circular singly linked lists as a solution. It describes the memory representation of a circular linked list using node pointers. It lists common operations on circular linked lists like insertion, deletion, searching and display. It provides examples of creating a new list, inserting nodes at the beginning and end, and deleting nodes from the beginning and end. It notes that a tail pointer should be used instead of the head pointer for certain operations.1. 4 Singly linked list deletion
1. 4 Singly linked list deletionP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
The document discusses various deletion operations on singly linked lists. It provides algorithms and code snippets for deleting the first element, last element, a given element, and an element at a given position in a singly linked list. The algorithms handle both empty lists and non-empty lists with single and multiple nodes. Key steps include checking for empty lists, traversing the list to find the required element or position while tracking the previous node, and updating pointers after deleting the node.1. 3 singly linked list insertion 2
1. 3 singly linked list insertion 2P. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
This document discusses algorithms for inserting nodes into singly linked lists at different positions. It provides pseudocode and illustrations for inserting a node after a given element, before a given element, and at a specified position.
For each insertion operation, it outlines the steps: 1) create a new node, 2) get the data, 3) check for empty list or specified position and traverse the list to find the insertion point. It then shows how to insert the new node by adjusting the next pointers of the preceding and current nodes. Examples are given with initial and final list illustrations for each insertion type.1. 2 Singly Linked List
The document discusses singly linked lists and their advantages over arrays. It describes a lecture on data structures covering singly linked lists. The lecture explains what is wrong with static memory allocation in arrays and how linked lists provide a dynamic structure using nodes and pointers. It shows examples of creating linked list nodes, inserting nodes at the beginning and end of a list, and traversals through a non-empty list. The key operations on singly linked lists like creation, insertion, deletion and traversal are also summarized.
1. C Basics for Data Structures Bridge Course
1. C Basics for Data Structures Bridge CourseP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
This document provides an overview of a 5-day training course on C basics for data structures. The course will cover topics such as arrays, pointers, and structures. Specific topics for arrays include how they are stored in memory, static versus dynamic allocation, and pointers to arrays. Pointers will cover what they are, pointer arithmetic, and pointers to pointers. The document includes example code for allocating memory dynamically using malloc and free.Approximation Algorithms TSP
Approximation Algorithms TSPP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
This document discusses approximation algorithms for solving NP-hard problems like the traveling salesman problem (TSP) and knapsack problem. It provides an overview of approximation algorithms, defining them as polynomial-time algorithms that provide good but not necessarily optimal solutions. The document then focuses on approximation algorithms for the TSP, describing greedy algorithms like nearest neighbor, minimum spanning tree based algorithms like Christofides, and local search heuristics like 2-opt and Lin-Kernighan. It concludes by noting some applications of approximating the TSP.Optimal binary search tree dynamic programming
Optimal binary search tree dynamic programmingP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
The document discusses optimal binary search trees (OBST) and describes the process of creating one. It begins by introducing OBST and noting that the method can minimize average number of comparisons in a successful search. It then shows the step-by-step process of calculating the costs for different partitions to arrive at the optimal binary search tree for a given sample dataset with keys and frequencies. The process involves calculating Catalan numbers for each partition and choosing the minimum cost at each step as the optimal is determined.The stable marriage problem iterative improvement method
The stable marriage problem iterative improvement methodP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
The document discusses the stable marriage problem and its iterative improvement method. It describes a stable marriage as a pairing between two disjoint sets where each member of one set is paired with exactly one member of the other set. It states that the stable marriage algorithm terminates with a stable marriage output after no more than n^2 iterations. Some applications of stable marriage problems mentioned include assigning students to universities and doctors to hospitals.Maximum matching in bipartite graphs iterative improvement method
Maximum matching in bipartite graphs iterative improvement methodP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
The document discusses two approaches for finding the maximum matching in bipartite graphs - Ford Fulkerson's augmenting path method and the shortest augmenting path method using breadth-first search (BFS). It provides examples of applying both methods to a sample graph, finding augmenting paths and increasing the number of matching pairs at each step until reaching the maximum matching.Knapsack dynamic programming formula top down (1)
Knapsack dynamic programming formula top down (1)P. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
The document describes the dynamic programming approach to solving the 0/1 knapsack problem. It provides the recursive formula for calculating F(i,Cj), which represents the maximum profit for a knapsack of capacity Cj using items 1 to i. It works through an example calculation of F(i,Cj) by recursively applying the formula and breaking it down into sub-problems.Knapsack dynamic programming formula bottom up
Knapsack dynamic programming formula bottom upP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
The document describes the 0/1 knapsack problem and its dynamic programming solution. It presents the bottom-up dynamic programming approach, showing a table that is populated with the maximum profits possible given item weights and the knapsack capacity. The table is populated row-by-row, considering all possible item combinations. Finally, it traces back through the table to find the optimal set of items to include in the knapsack.Huffman tree coding greedy approach
Huffman tree coding greedy approachP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
The document discusses Huffman tree coding, which is a variable-length encoding technique used for lossless data compression. It assigns variable-length codewords to input characters based on their frequency of occurrence, with more common characters represented by shorter codewords. The document provides an example of constructing a Huffman tree for an alphabet and encoding and decoding sample text strings using the generated Huffman codes.Simplex method
The document describes the simplex method for solving linear programming problems. It provides an example problem in standard form and walks through two iterations of the simplex method to find the optimal solution. The key steps of the simplex method include identifying the entering and departing variables, calculating the pivot element, pivoting the rows, and checking for convergence. After two iterations, the example problem converges to an optimal objective value of 14 when x=3 and y=1.
Simplex method
The document describes the simplex method for solving linear programming problems. It involves iteratively improving the solution by moving from one vertex of the feasible region to an adjacent vertex with a better objective value. The method works by representing the problem in standard form and then performing pivoting steps. It begins with an initial basic feasible solution and moves to adjacent basic feasible solutions until reaching the optimal solution.
Multiplication of integers & strassens matrix multiplication subi notes
Multiplication of integers & strassens matrix multiplication subi notesP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
Divide and Conquer TechniqueHuffman tree coding
The document discusses Huffman tree coding, which is a variable-length encoding technique that assigns codewords of different lengths to symbols based on their frequency of occurrence. It explains how to create a Huffman tree from symbol frequencies, derive the Huffman codes, and use the codes to encode and decode messages. An example is provided where the frequencies of letters in an alphabet are used to generate a Huffman tree and codes, and a message is encoded and decoded to demonstrate the process.
Final maximum matching in bipartite graphs
Final maximum matching in bipartite graphsP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
The document discusses Ford Fulkerson's augmenting path method for finding maximum matchings in bipartite graphs. It explains how the algorithm works by finding augmenting paths between unmatched node pairs and incrementally increasing the size of the matching. The document also provides an example applying the shortest augmenting path algorithm using breadth-first search to find a maximum matching in a sample bipartite graph.The Stable Marriage Problem
The Stable Marriage ProblemP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
The document discusses the stable marriage problem and its iterative improvement method. It defines a stable marriage matching as a one-to-one pairing between two disjoint sets where each member of each set is paired with exactly one member of the other set. It notes that a blocking pair occurs when two members who are not currently paired both prefer each other over their current partners. The document states that the stable marriage algorithm terminates in no more than n^2 iterations with a stable marriage output. It lists several applications of the stable marriage problem including student to university matching, doctors to hospitals matching, and student to project guide matching.5. cs8451 daa anna univ question bank unit 5
5. cs8451 daa anna univ question bank unit 5P. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
1. The document discusses algorithms for coping with limitations of algorithm power, including NP-complete, NP-hard, and approximation algorithms. It provides questions, answers, and examples related to backtracking, branch and bound, travelling salesman problem, knapsack problem, and other algorithm design techniques.
2. Specific topics covered include the n-queen problem, Hamiltonian circuit problem, subset sum problem, assignment problem, approximation algorithms for NP-hard problems like TSP and knapsack. Algorithms like nearest neighbor and multi-fragment heuristics for TSP are also mentioned.
3. 66 questions related to algorithm analysis, complexity classes, approximation algorithms and specific problems are provided at different difficulty levels to assess students4. cs8451 daa anna univ question bank unit 4
4. cs8451 daa anna univ question bank unit 4P. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
This document contains question banks related to the subject "Design & Analysis of Algorithms". It is divided into three parts:
Part A contains 14 multiple choice questions related to iterative improvement techniques like the simplex method, maximum flow problems, maximum matching in bipartite graphs, and stable marriage problems.
Part B contains 28 questions requiring longer answers on these same topics. Questions require explaining concepts like the steps of the simplex method, applying algorithms like Ford-Fulkerson, and analyzing algorithms' time complexities.
Part C contains 1 question asking to use the simplex method to solve an optimization problem about maximizing a farmer's profit by allocating land between rice and wheat crops.3. cs8451 daa anna univ question bank unit 3
3. cs8451 daa anna univ question bank unit 3P. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
This document contains questions from previous years' exams on the subject of algorithms, specifically regarding dynamic programming, greedy techniques, and related algorithms. It is divided into multiple parts covering different topics:
- Part A contains short answer and descriptive questions on dynamic programming techniques like coin changing, Floyd's algorithm, knapsack problem, and greedy algorithms.
- Part B contains longer descriptive questions requiring explanations and examples regarding dynamic programming, Floyd's algorithm, optimal binary search trees, knapsack problem, traveling salesman problem, and greedy algorithms like Prim's, Kruskal's and Dijkstra's algorithms.
- Part C contains even more in-depth questions solving problems using dynamic programming, Warshall's algorithm2. cs8451 daa anna univ question bank unit 2
2. cs8451 daa anna univ question bank unit 2P. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
This document contains a question bank for the subject "Design & Analysis of Algorithms" covering topics like brute force algorithms, string matching, closest pair problems, divide and conquer methods, binary search, merge sort, quicksort, and convex hull problems. It includes over 20 questions ranging from 1-16 marks each, testing different levels of knowledge. For each topic area, questions assess algorithms, complexity analysis, and examples. The document is organized into three parts with questions testing basic understanding, writing algorithms, and analyzing algorithms respectively.
Human: Thank you for the summary. You captured the key aspects of the document concisely in 3 sentences as requested.1. cs8451 daa anna univ question bank unit 1
1. cs8451 daa anna univ question bank unit 1P. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
This document contains questions from the Design and Analysis of Algorithms subject for the second semester of the II IT class. It covers topics like brute force algorithms, divide and conquer algorithms, abstract data types, arrays, linked lists, queues, stacks, polynomials, and more. There are a total of 41 questions ranging from 2 to 16 marks testing different levels of knowledge. The questions are divided into three parts - introduction to data structures, implementation of various data structures like arrays, linked lists, queues, stacks, and polynomials.More Related Content
More from P. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
1. 2 Singly Linked List
The document discusses singly linked lists and their advantages over arrays. It describes a lecture on data structures covering singly linked lists. The lecture explains what is wrong with static memory allocation in arrays and how linked lists provide a dynamic structure using nodes and pointers. It shows examples of creating linked list nodes, inserting nodes at the beginning and end of a list, and traversals through a non-empty list. The key operations on singly linked lists like creation, insertion, deletion and traversal are also summarized.
1. C Basics for Data Structures Bridge Course
1. C Basics for Data Structures Bridge CourseP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
This document provides an overview of a 5-day training course on C basics for data structures. The course will cover topics such as arrays, pointers, and structures. Specific topics for arrays include how they are stored in memory, static versus dynamic allocation, and pointers to arrays. Pointers will cover what they are, pointer arithmetic, and pointers to pointers. The document includes example code for allocating memory dynamically using malloc and free.Approximation Algorithms TSP
Approximation Algorithms TSPP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
This document discusses approximation algorithms for solving NP-hard problems like the traveling salesman problem (TSP) and knapsack problem. It provides an overview of approximation algorithms, defining them as polynomial-time algorithms that provide good but not necessarily optimal solutions. The document then focuses on approximation algorithms for the TSP, describing greedy algorithms like nearest neighbor, minimum spanning tree based algorithms like Christofides, and local search heuristics like 2-opt and Lin-Kernighan. It concludes by noting some applications of approximating the TSP.Optimal binary search tree dynamic programming
Optimal binary search tree dynamic programmingP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
The document discusses optimal binary search trees (OBST) and describes the process of creating one. It begins by introducing OBST and noting that the method can minimize average number of comparisons in a successful search. It then shows the step-by-step process of calculating the costs for different partitions to arrive at the optimal binary search tree for a given sample dataset with keys and frequencies. The process involves calculating Catalan numbers for each partition and choosing the minimum cost at each step as the optimal is determined.The stable marriage problem iterative improvement method
The stable marriage problem iterative improvement methodP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
The document discusses the stable marriage problem and its iterative improvement method. It describes a stable marriage as a pairing between two disjoint sets where each member of one set is paired with exactly one member of the other set. It states that the stable marriage algorithm terminates with a stable marriage output after no more than n^2 iterations. Some applications of stable marriage problems mentioned include assigning students to universities and doctors to hospitals.Maximum matching in bipartite graphs iterative improvement method
Maximum matching in bipartite graphs iterative improvement methodP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
The document discusses two approaches for finding the maximum matching in bipartite graphs - Ford Fulkerson's augmenting path method and the shortest augmenting path method using breadth-first search (BFS). It provides examples of applying both methods to a sample graph, finding augmenting paths and increasing the number of matching pairs at each step until reaching the maximum matching.Knapsack dynamic programming formula top down (1)
Knapsack dynamic programming formula top down (1)P. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
The document describes the dynamic programming approach to solving the 0/1 knapsack problem. It provides the recursive formula for calculating F(i,Cj), which represents the maximum profit for a knapsack of capacity Cj using items 1 to i. It works through an example calculation of F(i,Cj) by recursively applying the formula and breaking it down into sub-problems.Knapsack dynamic programming formula bottom up
Knapsack dynamic programming formula bottom upP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
The document describes the 0/1 knapsack problem and its dynamic programming solution. It presents the bottom-up dynamic programming approach, showing a table that is populated with the maximum profits possible given item weights and the knapsack capacity. The table is populated row-by-row, considering all possible item combinations. Finally, it traces back through the table to find the optimal set of items to include in the knapsack.Huffman tree coding greedy approach
Huffman tree coding greedy approachP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
The document discusses Huffman tree coding, which is a variable-length encoding technique used for lossless data compression. It assigns variable-length codewords to input characters based on their frequency of occurrence, with more common characters represented by shorter codewords. The document provides an example of constructing a Huffman tree for an alphabet and encoding and decoding sample text strings using the generated Huffman codes.Simplex method
The document describes the simplex method for solving linear programming problems. It provides an example problem in standard form and walks through two iterations of the simplex method to find the optimal solution. The key steps of the simplex method include identifying the entering and departing variables, calculating the pivot element, pivoting the rows, and checking for convergence. After two iterations, the example problem converges to an optimal objective value of 14 when x=3 and y=1.
Simplex method
The document describes the simplex method for solving linear programming problems. It involves iteratively improving the solution by moving from one vertex of the feasible region to an adjacent vertex with a better objective value. The method works by representing the problem in standard form and then performing pivoting steps. It begins with an initial basic feasible solution and moves to adjacent basic feasible solutions until reaching the optimal solution.
Multiplication of integers & strassens matrix multiplication subi notes
Multiplication of integers & strassens matrix multiplication subi notesP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
Divide and Conquer TechniqueHuffman tree coding
The document discusses Huffman tree coding, which is a variable-length encoding technique that assigns codewords of different lengths to symbols based on their frequency of occurrence. It explains how to create a Huffman tree from symbol frequencies, derive the Huffman codes, and use the codes to encode and decode messages. An example is provided where the frequencies of letters in an alphabet are used to generate a Huffman tree and codes, and a message is encoded and decoded to demonstrate the process.
Final maximum matching in bipartite graphs
Final maximum matching in bipartite graphsP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
The document discusses Ford Fulkerson's augmenting path method for finding maximum matchings in bipartite graphs. It explains how the algorithm works by finding augmenting paths between unmatched node pairs and incrementally increasing the size of the matching. The document also provides an example applying the shortest augmenting path algorithm using breadth-first search to find a maximum matching in a sample bipartite graph.The Stable Marriage Problem
The Stable Marriage ProblemP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
The document discusses the stable marriage problem and its iterative improvement method. It defines a stable marriage matching as a one-to-one pairing between two disjoint sets where each member of each set is paired with exactly one member of the other set. It notes that a blocking pair occurs when two members who are not currently paired both prefer each other over their current partners. The document states that the stable marriage algorithm terminates in no more than n^2 iterations with a stable marriage output. It lists several applications of the stable marriage problem including student to university matching, doctors to hospitals matching, and student to project guide matching.5. cs8451 daa anna univ question bank unit 5
5. cs8451 daa anna univ question bank unit 5P. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
1. The document discusses algorithms for coping with limitations of algorithm power, including NP-complete, NP-hard, and approximation algorithms. It provides questions, answers, and examples related to backtracking, branch and bound, travelling salesman problem, knapsack problem, and other algorithm design techniques.
2. Specific topics covered include the n-queen problem, Hamiltonian circuit problem, subset sum problem, assignment problem, approximation algorithms for NP-hard problems like TSP and knapsack. Algorithms like nearest neighbor and multi-fragment heuristics for TSP are also mentioned.
3. 66 questions related to algorithm analysis, complexity classes, approximation algorithms and specific problems are provided at different difficulty levels to assess students4. cs8451 daa anna univ question bank unit 4
4. cs8451 daa anna univ question bank unit 4P. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
This document contains question banks related to the subject "Design & Analysis of Algorithms". It is divided into three parts:
Part A contains 14 multiple choice questions related to iterative improvement techniques like the simplex method, maximum flow problems, maximum matching in bipartite graphs, and stable marriage problems.
Part B contains 28 questions requiring longer answers on these same topics. Questions require explaining concepts like the steps of the simplex method, applying algorithms like Ford-Fulkerson, and analyzing algorithms' time complexities.
Part C contains 1 question asking to use the simplex method to solve an optimization problem about maximizing a farmer's profit by allocating land between rice and wheat crops.3. cs8451 daa anna univ question bank unit 3
3. cs8451 daa anna univ question bank unit 3P. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
This document contains questions from previous years' exams on the subject of algorithms, specifically regarding dynamic programming, greedy techniques, and related algorithms. It is divided into multiple parts covering different topics:
- Part A contains short answer and descriptive questions on dynamic programming techniques like coin changing, Floyd's algorithm, knapsack problem, and greedy algorithms.
- Part B contains longer descriptive questions requiring explanations and examples regarding dynamic programming, Floyd's algorithm, optimal binary search trees, knapsack problem, traveling salesman problem, and greedy algorithms like Prim's, Kruskal's and Dijkstra's algorithms.
- Part C contains even more in-depth questions solving problems using dynamic programming, Warshall's algorithm2. cs8451 daa anna univ question bank unit 2
2. cs8451 daa anna univ question bank unit 2P. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
This document contains a question bank for the subject "Design & Analysis of Algorithms" covering topics like brute force algorithms, string matching, closest pair problems, divide and conquer methods, binary search, merge sort, quicksort, and convex hull problems. It includes over 20 questions ranging from 1-16 marks each, testing different levels of knowledge. For each topic area, questions assess algorithms, complexity analysis, and examples. The document is organized into three parts with questions testing basic understanding, writing algorithms, and analyzing algorithms respectively.
Human: Thank you for the summary. You captured the key aspects of the document concisely in 3 sentences as requested.1. cs8451 daa anna univ question bank unit 1
1. cs8451 daa anna univ question bank unit 1P. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
This document contains questions from the Design and Analysis of Algorithms subject for the second semester of the II IT class. It covers topics like brute force algorithms, divide and conquer algorithms, abstract data types, arrays, linked lists, queues, stacks, polynomials, and more. There are a total of 41 questions ranging from 2 to 16 marks testing different levels of knowledge. The questions are divided into three parts - introduction to data structures, implementation of various data structures like arrays, linked lists, queues, stacks, and polynomials.More from P. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai (20)
The stable marriage problem iterative improvement method
The stable marriage problem iterative improvement method
Maximum matching in bipartite graphs iterative improvement method
Maximum matching in bipartite graphs iterative improvement method
Multiplication of integers & strassens matrix multiplication subi notes
Multiplication of integers & strassens matrix multiplication subi notes
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Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Low power architecture of logic gates using adiabatic techniques
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
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New techniques for characterising damage in rock slopes.pdf
rock mass characterization and New techniques for characterising damage in rock slopes
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