This is a very important type of algorithm paradigm which is mostly used to solve any kind of problems like sorting ( merge sort, quick sort), binary search, Tower of Hanoi, etc.
Divide and Conquer Algorithms - D&C forms a distinct algorithm design technique in computer science, wherein a problem is solved by repeatedly invoking the algorithm on smaller occurrences of the same problem. Binary search, merge sort, Euclid's algorithm can all be formulated as examples of divide and conquer algorithms. Strassen's algorithm and Nearest Neighbor algorithm are two other examples.
This is the second lecture in the CS 6212 class. Covers asymptotic notation and data structures. Also outlines the coming lectures wherein we will study the various algorithm design techniques.
Real life use of Discrete Mathematics and Digital electronics. Niloy Biswas
We made a presentation about where we use Discrete math and Digital electronics in our real life. It's real life application of Discrete math and Digital electronics.
The document discusses algorithms and data structures. It begins with an introduction to merge sort, solving recurrences, and the master theorem for analyzing divide-and-conquer algorithms. It then covers quicksort and heaps. The last part discusses heaps in more detail and provides an example heap representation as a complete binary tree.
The brute force algorithm finds the maximum subarray of a given array by calculating the sum of all possible contiguous subarrays. It has a time complexity of O(n^2) as it calculates the sum in two nested loops from index 1 to n. While simple to implement, the brute force approach is only efficient for small problem sizes due to its quadratic time complexity. More optimized algorithms are needed for large arrays.
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.
1) The document discusses complexity analysis of algorithms, which involves determining the time efficiency of algorithms by counting the number of basic operations performed based on input size.
2) It covers motivations for complexity analysis, machine independence, and analyzing best, average, and worst case complexities.
3) Simple rules are provided for determining the complexity of code structures like loops, nested loops, if/else statements, and switch cases based on the number of iterations and branching.
Theory of computation deals with analyzing the capabilities and limitations of computers. It has three main branches: automata theory, computability theory, and computational complexity theory. A model of computation defines the basic operations and costs of a computing system. More powerful models of computation, like Turing machines with random access memory, can solve more complex problems than simpler models like finite automata. However, even Turing machines cannot solve all computational problems - some problems are unsolvable.
This document provides an overview of dynamic programming. It begins by explaining that dynamic programming is a technique for solving optimization problems by breaking them down into overlapping subproblems and storing the results of solved subproblems in a table to avoid recomputing them. It then provides examples of problems that can be solved using dynamic programming, including Fibonacci numbers, binomial coefficients, shortest paths, and optimal binary search trees. The key aspects of dynamic programming algorithms, including defining subproblems and combining their solutions, are also outlined.
The document discusses the divide and conquer algorithm design paradigm. It begins by defining divide and conquer as recursively breaking down a problem into smaller sub-problems, solving the sub-problems, and then combining the solutions to solve the original problem. Some examples of problems that can be solved using divide and conquer include binary search, quicksort, merge sort, and the fast Fourier transform algorithm. The document then discusses control abstraction, efficiency analysis, and uses divide and conquer to provide algorithms for large integer multiplication and merge sort. It concludes by defining the convex hull problem and providing an example input and output.
The document discusses disjoint set data structures and union-find algorithms. Disjoint set data structures track partitions of elements into separate, non-overlapping sets. Union-find algorithms perform two operations on these data structures: find, to determine which set an element belongs to; and union, to combine two sets into a single set. The document describes array-based representations of disjoint sets and algorithms for the union and find operations, including a weighted union algorithm that aims to keep trees relatively balanced by favoring attaching the smaller tree to the root of the larger tree.
Lecture: Regular Expressions and Regular LanguagesMarina Santini
This document provides an introduction to regular expressions and regular languages. It defines the key operations used in regular expressions: union, concatenation, and Kleene star. It explains how regular expressions can be converted into finite state automata and vice versa. Examples of regular expressions are provided. The document also defines regular languages as those languages that can be accepted by a deterministic finite automaton. It introduces the pumping lemma as a way to determine if a language is not regular. Finally, it includes some practical activities for readers to practice converting regular expressions to automata and writing regular expressions.
The document discusses greedy algorithms, their characteristics, and an example problem. Greedy algorithms make locally optimal choices at each step in the hope of finding a global optimum. They are simpler and faster than dynamic programming but may not always find the true optimal solution. The coin changing problem is used to illustrate a greedy approach of always selecting the largest valid coin denomination at each step.
1. This document outlines an introduction to machine learning lecture by Dr. Varun Kumar. It discusses examples of machine learning, attributes in machine learning applications, and examples such as classification, regression, supervised vs unsupervised learning.
2. Machine learning can analyze large amounts of data from sciences, the world wide web, and adapt to changes without needing every situation predefined. It involves programming computers to optimize performance using example data.
3. Attributes in machine learning applications map the input to the output through mathematical functions. Examples given include factors that influence data transmission rates in wireless communication.
The document discusses several key process scheduling policies and algorithms:
1. Maximum throughput, minimize response time, and other policies aim to optimize different performance metrics like job completion time.
2. Common scheduling algorithms include first come first served (FCFS), shortest job next (SJN), priority scheduling, round robin, and multilevel queues. Each has advantages for different workload types.
3. The document also covers process synchronization challenges like deadlock and livelock that can occur when processes contend for shared resources in certain ordering. Methods to avoid or recover from such issues are important for system design.
Parallel algorithms can be specifically written to execute on computers with multiple processors. They are often modeled using parallel random-access machines (PRAM) which allow for an unlimited number of processors that can access shared memory uniformly. Common parallel algorithms include matrix multiplication, merge sort, and shortest path algorithms like Floyd's algorithm.
The document describes the quicksort algorithm. Quicksort works by:
1) Partitioning the array around a pivot element into two sub-arrays of less than or equal and greater than elements.
2) Recursively sorting the two sub-arrays.
3) Combining the now sorted sub-arrays.
In the average case, quicksort runs in O(n log n) time due to balanced partitions at each recursion level. However, in the worst case of an already sorted input, it runs in O(n^2) time due to highly unbalanced partitions. A randomized version of quicksort chooses pivots randomly to avoid worst case behavior.
Introduction to datastructure and algorithmPratik Mota
Introduction to data structure and algorithm
-Basics of Data Structure and Algorithm
-Practical Examples of where Data Structure Algorithms is used
-Asymptotic Notations [ O(n), o(n), θ(n), Ω(n), ω(n) ]
-Calculation of Time and Space Complexity
-GNU gprof basic
What Is Dynamic Programming? | Dynamic Programming Explained | Programming Fo...Simplilearn
This presentation on 'What Is Dynamic Programming?' will acquaint you with a clear understanding of how this programming paradigm works with the help of a real-life example. In this Dynamic Programming Tutorial, you will understand why recursion is not compatible and how you can solve the problems involved in recursion using DP. Finally, we will cover the dynamic programming implementation of the Fibonacci series program. So, let's get started!
The topics covered in this presentation are:
1. Introduction
2. Real-Life Example of Dynamic Programming
3. Introduction to Dynamic Programming
4. Dynamic Programming Interpretation of Fibonacci Series Program
5. How Does Dynamic Programming Work?
What Is Dynamic Programming?
In computer science, something is said to be efficient if it is quick and uses minimal memory. By storing the solutions to subproblems, we can quickly look them up if the same problem arises again. Because there is no need to recompute the solution, this saves a significant amount of calculation time. But hold on! Efficiency comprises both time and space difficulty. But, why does it matter if we reduce the time required to solve the problem only to increase the space required? This is why it is critical to realize that the ultimate goal of Dynamic Programming is to obtain considerably quicker calculation time at the price of a minor increase in space utilized. Dynamic programming is defined as an algorithmic paradigm that solves a given complex problem by breaking it into several sub-problems and storing the results of those sub-problems to avoid the computation of the same sub-problem over and over again.
What is Programming?
Programming is an act of designing, developing, deploying an executlable software solution to the given user-defined problem.
Programming involves the following stages.
- Problem Statement
- Algorithms and Flowcharts
- Coding the program
- Debug the program.
- Documention
- Maintainence
Simplilearn’s Python Training Course is an all-inclusive program that will introduce you to the Python development language and expose you to the essentials of object-oriented programming, web development with Django and game development. Python has surpassed Java as the top language used to introduce U.S.
Learn more at: https://www.simplilearn.com/mobile-and-software-development/python-development-training
This document discusses the merge sort algorithm for sorting a sequence of numbers. It begins by introducing the divide and conquer approach, which merge sort uses. It then provides an example of how merge sort works, dividing the sequence into halves, sorting the halves recursively, and then merging the sorted halves together. The document proceeds to provide pseudocode for the merge sort and merge algorithms. It analyzes the running time of merge sort using recursion trees, determining that it runs in O(n log n) time. Finally, it covers techniques for solving recurrence relations that arise in algorithms like divide and conquer approaches.
Given weights and values of n items, put these items in a knapsack of capacity W to get the maximum total value in the knapsack. In other words, given two integer arrays val[0..n-1] and wt[0..n-1] which represent values and weights associated with n items respectively. Also given an integer W which represents knapsack capacity, find out the maximum value subset of val[] such that sum of the weights of this subset is smaller than or equal to W. You cannot break an item, either pick the complete item or don’t pick it (0-1 property).
Method 1: Recursion by Brute-Force algorithm OR Exhaustive Search.
Approach: A simple solution is to consider all subsets of items and calculate the total weight and value of all subsets. Consider the only subsets whose total weight is smaller than W. From all such subsets, pick the maximum value subset.
Optimal Sub-structure: To consider all subsets of items, there can be two cases for every item.
Case 1: The item is included in the optimal subset.
Case 2: The item is not included in the optimal set.
Therefore, the maximum value that can be obtained from ‘n’ items is the max of the following two values.
Maximum value obtained by n-1 items and W weight (excluding nth item).
Value of nth item plus maximum value obtained by n-1 items and W minus the weight of the nth item (including nth item).
If the weight of the ‘nth’ item is greater than ‘W’, then the nth item cannot be included and Case 1 is the only possibility.
This lecture slide contains:
- Difference between FA, PDA and TM
- Formal definition of TM
- TM transition function and configuration
- Designing TM for different languages
- Simulating TM for different strings
This slide deck is used as an introduction to Relational Algebra and its relation to the MapReduce programming model, as part of the Distributed Systems and Cloud Computing course I hold at Eurecom.
Course website:
http://michiard.github.io/DISC-CLOUD-COURSE/
Sources available here:
https://github.com/michiard/DISC-CLOUD-COURSE
L03 ai - knowledge representation using logicManjula V
The document discusses knowledge representation using predicate logic. It begins by reviewing propositional logic and its semantics using truth tables. It then introduces predicate logic, which can represent properties and relations using predicates with arguments. It discusses representing knowledge in predicate logic using quantifiers, predicates, and variables. It also covers inferencing in predicate logic using techniques like forward chaining, backward chaining, and resolution. An example problem is presented to illustrate representing a problem and solving it using resolution refutation in predicate logic.
This document provides an overview of deterministic finite automata (DFA) through examples and practice problems. It begins with defining the components of a DFA, including states, alphabet, transition function, start state, and accepting states. An example DFA is given to recognize strings ending in "00". Additional practice problems involve drawing minimal DFAs, determining the minimum number of states for a language, and completing partially drawn DFAs. The document aims to help students learn and practice working with DFA models.
The document discusses various algorithms design approaches and patterns including divide and conquer, greedy algorithms, dynamic programming, backtracking, and branch and bound. It provides examples of each along with pseudocode. Specific algorithms discussed include binary search, merge sort, knapsack problem, shortest path problems, and the traveling salesman problem. The document is authored by Ashwin Shiv, a second year computer science student at NIT Delhi.
Divide and conquer is an algorithm design paradigm where a problem is broken into smaller subproblems, those subproblems are solved independently, and then their results are combined to solve the original problem. Some examples of algorithms that use this approach are merge sort, quicksort, and matrix multiplication algorithms like Strassen's algorithm. The greedy method works in stages, making locally optimal choices at each step in the hope of finding a global optimum. It is used for problems like job sequencing with deadlines and the knapsack problem. Minimum cost spanning trees find subgraphs of connected graphs that include all vertices using a minimum number of edges.
This document provides an overview of dynamic programming. It begins by explaining that dynamic programming is a technique for solving optimization problems by breaking them down into overlapping subproblems and storing the results of solved subproblems in a table to avoid recomputing them. It then provides examples of problems that can be solved using dynamic programming, including Fibonacci numbers, binomial coefficients, shortest paths, and optimal binary search trees. The key aspects of dynamic programming algorithms, including defining subproblems and combining their solutions, are also outlined.
The document discusses the divide and conquer algorithm design paradigm. It begins by defining divide and conquer as recursively breaking down a problem into smaller sub-problems, solving the sub-problems, and then combining the solutions to solve the original problem. Some examples of problems that can be solved using divide and conquer include binary search, quicksort, merge sort, and the fast Fourier transform algorithm. The document then discusses control abstraction, efficiency analysis, and uses divide and conquer to provide algorithms for large integer multiplication and merge sort. It concludes by defining the convex hull problem and providing an example input and output.
The document discusses disjoint set data structures and union-find algorithms. Disjoint set data structures track partitions of elements into separate, non-overlapping sets. Union-find algorithms perform two operations on these data structures: find, to determine which set an element belongs to; and union, to combine two sets into a single set. The document describes array-based representations of disjoint sets and algorithms for the union and find operations, including a weighted union algorithm that aims to keep trees relatively balanced by favoring attaching the smaller tree to the root of the larger tree.
Lecture: Regular Expressions and Regular LanguagesMarina Santini
This document provides an introduction to regular expressions and regular languages. It defines the key operations used in regular expressions: union, concatenation, and Kleene star. It explains how regular expressions can be converted into finite state automata and vice versa. Examples of regular expressions are provided. The document also defines regular languages as those languages that can be accepted by a deterministic finite automaton. It introduces the pumping lemma as a way to determine if a language is not regular. Finally, it includes some practical activities for readers to practice converting regular expressions to automata and writing regular expressions.
The document discusses greedy algorithms, their characteristics, and an example problem. Greedy algorithms make locally optimal choices at each step in the hope of finding a global optimum. They are simpler and faster than dynamic programming but may not always find the true optimal solution. The coin changing problem is used to illustrate a greedy approach of always selecting the largest valid coin denomination at each step.
1. This document outlines an introduction to machine learning lecture by Dr. Varun Kumar. It discusses examples of machine learning, attributes in machine learning applications, and examples such as classification, regression, supervised vs unsupervised learning.
2. Machine learning can analyze large amounts of data from sciences, the world wide web, and adapt to changes without needing every situation predefined. It involves programming computers to optimize performance using example data.
3. Attributes in machine learning applications map the input to the output through mathematical functions. Examples given include factors that influence data transmission rates in wireless communication.
The document discusses several key process scheduling policies and algorithms:
1. Maximum throughput, minimize response time, and other policies aim to optimize different performance metrics like job completion time.
2. Common scheduling algorithms include first come first served (FCFS), shortest job next (SJN), priority scheduling, round robin, and multilevel queues. Each has advantages for different workload types.
3. The document also covers process synchronization challenges like deadlock and livelock that can occur when processes contend for shared resources in certain ordering. Methods to avoid or recover from such issues are important for system design.
Parallel algorithms can be specifically written to execute on computers with multiple processors. They are often modeled using parallel random-access machines (PRAM) which allow for an unlimited number of processors that can access shared memory uniformly. Common parallel algorithms include matrix multiplication, merge sort, and shortest path algorithms like Floyd's algorithm.
The document describes the quicksort algorithm. Quicksort works by:
1) Partitioning the array around a pivot element into two sub-arrays of less than or equal and greater than elements.
2) Recursively sorting the two sub-arrays.
3) Combining the now sorted sub-arrays.
In the average case, quicksort runs in O(n log n) time due to balanced partitions at each recursion level. However, in the worst case of an already sorted input, it runs in O(n^2) time due to highly unbalanced partitions. A randomized version of quicksort chooses pivots randomly to avoid worst case behavior.
Introduction to datastructure and algorithmPratik Mota
Introduction to data structure and algorithm
-Basics of Data Structure and Algorithm
-Practical Examples of where Data Structure Algorithms is used
-Asymptotic Notations [ O(n), o(n), θ(n), Ω(n), ω(n) ]
-Calculation of Time and Space Complexity
-GNU gprof basic
What Is Dynamic Programming? | Dynamic Programming Explained | Programming Fo...Simplilearn
This presentation on 'What Is Dynamic Programming?' will acquaint you with a clear understanding of how this programming paradigm works with the help of a real-life example. In this Dynamic Programming Tutorial, you will understand why recursion is not compatible and how you can solve the problems involved in recursion using DP. Finally, we will cover the dynamic programming implementation of the Fibonacci series program. So, let's get started!
The topics covered in this presentation are:
1. Introduction
2. Real-Life Example of Dynamic Programming
3. Introduction to Dynamic Programming
4. Dynamic Programming Interpretation of Fibonacci Series Program
5. How Does Dynamic Programming Work?
What Is Dynamic Programming?
In computer science, something is said to be efficient if it is quick and uses minimal memory. By storing the solutions to subproblems, we can quickly look them up if the same problem arises again. Because there is no need to recompute the solution, this saves a significant amount of calculation time. But hold on! Efficiency comprises both time and space difficulty. But, why does it matter if we reduce the time required to solve the problem only to increase the space required? This is why it is critical to realize that the ultimate goal of Dynamic Programming is to obtain considerably quicker calculation time at the price of a minor increase in space utilized. Dynamic programming is defined as an algorithmic paradigm that solves a given complex problem by breaking it into several sub-problems and storing the results of those sub-problems to avoid the computation of the same sub-problem over and over again.
What is Programming?
Programming is an act of designing, developing, deploying an executlable software solution to the given user-defined problem.
Programming involves the following stages.
- Problem Statement
- Algorithms and Flowcharts
- Coding the program
- Debug the program.
- Documention
- Maintainence
Simplilearn’s Python Training Course is an all-inclusive program that will introduce you to the Python development language and expose you to the essentials of object-oriented programming, web development with Django and game development. Python has surpassed Java as the top language used to introduce U.S.
Learn more at: https://www.simplilearn.com/mobile-and-software-development/python-development-training
This document discusses the merge sort algorithm for sorting a sequence of numbers. It begins by introducing the divide and conquer approach, which merge sort uses. It then provides an example of how merge sort works, dividing the sequence into halves, sorting the halves recursively, and then merging the sorted halves together. The document proceeds to provide pseudocode for the merge sort and merge algorithms. It analyzes the running time of merge sort using recursion trees, determining that it runs in O(n log n) time. Finally, it covers techniques for solving recurrence relations that arise in algorithms like divide and conquer approaches.
Given weights and values of n items, put these items in a knapsack of capacity W to get the maximum total value in the knapsack. In other words, given two integer arrays val[0..n-1] and wt[0..n-1] which represent values and weights associated with n items respectively. Also given an integer W which represents knapsack capacity, find out the maximum value subset of val[] such that sum of the weights of this subset is smaller than or equal to W. You cannot break an item, either pick the complete item or don’t pick it (0-1 property).
Method 1: Recursion by Brute-Force algorithm OR Exhaustive Search.
Approach: A simple solution is to consider all subsets of items and calculate the total weight and value of all subsets. Consider the only subsets whose total weight is smaller than W. From all such subsets, pick the maximum value subset.
Optimal Sub-structure: To consider all subsets of items, there can be two cases for every item.
Case 1: The item is included in the optimal subset.
Case 2: The item is not included in the optimal set.
Therefore, the maximum value that can be obtained from ‘n’ items is the max of the following two values.
Maximum value obtained by n-1 items and W weight (excluding nth item).
Value of nth item plus maximum value obtained by n-1 items and W minus the weight of the nth item (including nth item).
If the weight of the ‘nth’ item is greater than ‘W’, then the nth item cannot be included and Case 1 is the only possibility.
This lecture slide contains:
- Difference between FA, PDA and TM
- Formal definition of TM
- TM transition function and configuration
- Designing TM for different languages
- Simulating TM for different strings
This slide deck is used as an introduction to Relational Algebra and its relation to the MapReduce programming model, as part of the Distributed Systems and Cloud Computing course I hold at Eurecom.
Course website:
http://michiard.github.io/DISC-CLOUD-COURSE/
Sources available here:
https://github.com/michiard/DISC-CLOUD-COURSE
L03 ai - knowledge representation using logicManjula V
The document discusses knowledge representation using predicate logic. It begins by reviewing propositional logic and its semantics using truth tables. It then introduces predicate logic, which can represent properties and relations using predicates with arguments. It discusses representing knowledge in predicate logic using quantifiers, predicates, and variables. It also covers inferencing in predicate logic using techniques like forward chaining, backward chaining, and resolution. An example problem is presented to illustrate representing a problem and solving it using resolution refutation in predicate logic.
This document provides an overview of deterministic finite automata (DFA) through examples and practice problems. It begins with defining the components of a DFA, including states, alphabet, transition function, start state, and accepting states. An example DFA is given to recognize strings ending in "00". Additional practice problems involve drawing minimal DFAs, determining the minimum number of states for a language, and completing partially drawn DFAs. The document aims to help students learn and practice working with DFA models.
The document discusses various algorithms design approaches and patterns including divide and conquer, greedy algorithms, dynamic programming, backtracking, and branch and bound. It provides examples of each along with pseudocode. Specific algorithms discussed include binary search, merge sort, knapsack problem, shortest path problems, and the traveling salesman problem. The document is authored by Ashwin Shiv, a second year computer science student at NIT Delhi.
Divide and conquer is an algorithm design paradigm where a problem is broken into smaller subproblems, those subproblems are solved independently, and then their results are combined to solve the original problem. Some examples of algorithms that use this approach are merge sort, quicksort, and matrix multiplication algorithms like Strassen's algorithm. The greedy method works in stages, making locally optimal choices at each step in the hope of finding a global optimum. It is used for problems like job sequencing with deadlines and the knapsack problem. Minimum cost spanning trees find subgraphs of connected graphs that include all vertices using a minimum number of edges.
Merge Sort follows the divide and conquer approach to sort a list by recursively dividing it into smaller sub-lists, sorting those sub-lists using the same approach, and then merging the sorted sub-lists back together; it runs in O(n log n) time by breaking the problem into smaller problems of sorting each half of the list, combining the solutions by merging the halves back together; the document provides an overview of the merge sort algorithm and its use of divide and conquer to break down a sorting problem into subproblems.
dynamic programming complete by Mumtaz Ali (03154103173)Mumtaz Ali
The document discusses dynamic programming, including its meaning, definition, uses, techniques, and examples. Dynamic programming refers to breaking large problems down into smaller subproblems, solving each subproblem only once, and storing the results for future use. This avoids recomputing the same subproblems repeatedly. Examples covered include matrix chain multiplication, the Fibonacci sequence, and optimal substructure. The document provides details on formulating and solving dynamic programming problems through recursive definitions and storing results in tables.
Module 2ppt.pptx divid and conquer methodJyoReddy9
This document discusses dynamic programming and provides examples of problems that can be solved using dynamic programming. It covers the following key points:
- Dynamic programming can be used to solve problems that exhibit optimal substructure and overlapping subproblems. It works by breaking problems down into subproblems and storing the results of subproblems to avoid recomputing them.
- Examples of problems discussed include matrix chain multiplication, all pairs shortest path, optimal binary search trees, 0/1 knapsack problem, traveling salesperson problem, and flow shop scheduling.
- The document provides pseudocode for algorithms to solve matrix chain multiplication and optimal binary search trees using dynamic programming. It also explains the basic steps and principles of dynamic programming algorithm design
Dynamic programming is an algorithm design technique for optimization problems that reduces time by increasing space usage. It works by breaking problems down into overlapping subproblems and storing the solutions to subproblems, rather than recomputing them, to build up the optimal solution. The key aspects are identifying the optimal substructure of problems and handling overlapping subproblems in a bottom-up manner using tables. Examples that can be solved with dynamic programming include the knapsack problem, shortest paths, and matrix chain multiplication.
This document discusses complexity analysis of algorithms. It defines an algorithm and lists properties like being correct, unambiguous, terminating, and simple. It describes common algorithm design techniques like divide and conquer, dynamic programming, greedy method, and backtracking. It compares divide and conquer with dynamic programming. It discusses algorithm analysis in terms of time and space complexity to predict resource usage and compare algorithms. It introduces asymptotic notations like Big-O notation to describe upper bounds of algorithms as input size increases.
The document discusses the divide-and-conquer algorithm design paradigm. It defines divide-and-conquer as breaking a problem down into smaller subproblems, solving those subproblems recursively, and combining the solutions to solve the original problem. Examples of algorithms that use this approach include merge sort, quicksort, and matrix multiplication. Divide-and-conquer allows for problems to be solved in parallel and more efficiently uses memory caches. The closest pair problem is then presented as a detailed example of how a divide-and-conquer algorithm works to solve this problem in O(n log n) time compared to the brute force O(n2) approach.
The document discusses the divide-and-conquer algorithm design paradigm. It defines divide-and-conquer as breaking a problem down into smaller subproblems, solving those subproblems recursively, and combining the solutions to solve the original problem. Examples of algorithms that use this approach include merge sort, quicksort, and matrix multiplication. Advantages include solving difficult problems efficiently in parallel and with good memory performance. The document also provides an example of applying divide-and-conquer to the closest pair of points problem.
This document provides an introduction to integer programming, including:
- Integer programming models involve decision variables that must take on integer values, unlike linear programming which allows fractional values. Solving integer programs is more difficult.
- There are three types of integer programming models: pure integer, 0-1 integer, and mixed integer.
- Integer programming is used when non-integer solutions are impractical, like number of machines. Rounding solutions can affect costs significantly.
- Several examples of integer programming models are provided for problems like machine selection, facility location, and investment allocation.
- Two common solution methods are described: branch-and-bound and cutting-plane. Branch-and-bound systematically
Dynamic programming is a technique for solving complex problems by breaking them down into simpler sub-problems. It involves storing solutions to sub-problems for later use, avoiding recomputing them. Examples where it can be applied include matrix chain multiplication and calculating Fibonacci numbers. For matrix chains, dynamic programming finds the optimal order for multiplying matrices with minimum computations. For Fibonacci numbers, it calculates values in linear time by storing previous solutions rather than exponentially recomputing them through recursion.
2-Algorithms and Complexit data structurey.pdfishan743441
The document discusses algorithms design and complexity analysis. It defines an algorithm as a well-defined sequence of steps to solve a problem and notes that algorithms always take inputs and produce outputs. It discusses different approaches to designing algorithms like greedy, divide and conquer, and dynamic programming. It also covers analyzing algorithm complexity using asymptotic analysis by counting the number of basic operations and deriving the time complexity function in terms of input size.
Analysis of Algorithm II Unit version .pptxrajesshs31r
The document discusses the divide-and-conquer algorithm design paradigm. It defines divide-and-conquer as breaking a problem down into smaller subproblems, solving those subproblems recursively, and combining the solutions to solve the original problem. Examples provided include merge sort, quicksort, and the closest pair problem. Divide-and-conquer algorithms have advantages like being well-suited for parallelization and efficient memory usage.
Problem solving using computers - Chapter 1 To Sum It Up
The document discusses problem solving using computers. It begins with an introduction to problem solving, noting that writing a program involves writing instructions to solve a problem using a computer as a tool. It then outlines the typical steps in problem solving: defining the problem, analyzing it, coding a solution, debugging/testing, documenting, and developing an algorithm or approach. The document also discusses key concepts like algorithms, properties of algorithms, flowcharts, pseudocode, and complexity analysis. It provides examples of different algorithm types like brute force, recursive, greedy, and dynamic programming.
The document discusses dynamic programming and how it can be used to calculate the 20th term of the Fibonacci sequence. Dynamic programming breaks problems down into overlapping subproblems, solves each subproblem once, and stores the results for future use. It explains that the Fibonacci sequence can be calculated recursively with each term equal to the sum of the previous two. To calculate the 20th term, dynamic programming would calculate each preceding term only once and store the results, building up the solution from previously solved subproblems until it reaches the 20th term.
The document discusses three fundamental algorithms paradigms: recursion, divide-and-conquer, and dynamic programming. Recursion uses method calls to break down problems into simpler subproblems. Divide-and-conquer divides problems into independent subproblems, solves each, and combines solutions. Dynamic programming breaks problems into overlapping subproblems and builds up solutions, storing results of subproblems to avoid recomputing them. Examples like mergesort and calculating Fibonacci numbers are provided to illustrate the approaches.
Chart and graphs in R programming language CHANDAN KUMAR
This slide contains basics of charts and graphs in R programming language. I also focused on practical knowledge so I tried to give maximum example to understand the concepts.
RAID (Redundant Array of Independent Disks) technology was invented in 1987 to improve data storage performance and reliability. It combines multiple disk drive components into one or more logical units. There are different RAID levels that determine how disk arrays are used, with RAID 0 through RAID 6 being the standard levels. RAID levels use techniques like striping, mirroring, and parity to provide features like fault tolerance, high throughput, and data redundancy. Each level has advantages and disadvantages for performance, reliability, and capacity.
This presentation focus on the basic concept of pointers. so that students can easily understand. it also contains basic operations performed by pointer variables and implementation using c language.
This document provides an overview of sorting algorithms. It defines sorting as arranging data in a particular order like ascending or descending. Common sorting algorithms discussed include bubble sort, selection sort, insertion sort, merge sort, and quick sort. For each algorithm, the working method, implementation in C, time and space complexity is explained. The document also covers sorting terminology like stable vs unstable sorting and adaptive vs non-adaptive algorithms. Overall, the document serves as a comprehensive introduction to sorting and different sorting techniques.
Searching is an extremely fascinating and useful computer science technique. It helps to find the desired object with its location and number of occurrences. The presentation includes the basic principles, algorithms and c-language implementation.
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This document discusses algorithms and their properties. It defines an algorithm as a finite set of well-defined instructions to solve a problem. There are five criteria for writing algorithms: they must have inputs and outputs, be definite, finite, and effective. Algorithms use notation like step numbers, comments, and termination statements. Common algorithm types are dynamic programming, greedy, brute force, and divide and conquer. An example algorithm calculates the average of four numbers by reading inputs, computing the sum, calculating the average, and writing the output. Key patterns in algorithms are sequences, decisions, and repetitions.
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3. Divide and Conquer
It is an algorithm design paradigm which is based on
multi branch recursion.
The divide-and-conquer paradigm is often used to find
an optimal solution to a problem.
It is a technique to break a problem recursively into
sub-problems of the same or related type.
Sub-programs have to be simple enough to be solved
directly.
The solutions to these sub-problems are then
combined to solve the original problem
4. Divide and Conquer
D&C follow following three steps
Divide/Break- Break original problem into sub-
problems
Conquer/Solve - Solve every individual sub-problem,
recursively
Combine/Merge- Place the solutions to the sub-
problems together to solve the entire problem
5. Divide and Conquer
In short, D&C is a approach in which the entire
problem (i.e. too large to comprehend or overcome at
once) is divided into smaller pieces, the pieces are
solved separately, and all the pieces are combined
together.
7. Implementation
Program to calculate pow(x,n) in C language
#include<stdio.h>
#include<conio.h>
/* Function to calculate x raised to the power y */
int power(int x, unsigned int y)
{
if (y == 0)
return 1;
else if (y%2 == 0)
return power(x, y/2)*power(x, y/2);
9. Application
Following computer algorithms are based on divide
and conquer programming approach
Bineary Search
Merge Sort
Quick Sort
Maximum and Minimum Problem
Tower of Hanoi
Strassen's Matrix multiplication
Karatsuba Algorithm
10. Complexity
The complexity of the divide-and-conquer algorithm is
calculated using the master theorem.
T(n) = aT(n/b) + f(n), where
n = size of input
a = number of sub-problems in the recursion
n/b = size of each sub-problem. All sub-problems are
assumed to have the same size
f(n) = cost of the work done outside the recursive call,
which includes the cost of dividing the problem and
cost of merging the solutions
11. Advantages
The complexity for the multiplication of two matrices
using the naive method is O(n3), whereas using the
divide and conquer approach (ie. Strassen's matrix
multiplication) is O(n2.8074).
This strategy also simplifies other issues such as the
Tower of Hanoi.
Suitable for multiprocessing systems.
It does use memory caches effectively.