PPT On Sorting And Searching Concepts In Data Structure | In Programming Lang...Umesh Kumar
The document discusses various sorting and searching algorithms:
- Bubble sort, selection sort, merge sort, quicksort are sorting algorithms that arrange data in a particular order like numerical or alphabetical.
- Linear search and binary search are searching algorithms where linear search sequentially checks each item while binary search divides the data set in half with each comparison.
- Examples are provided to illustrate how each algorithm works step-by-step on sample data sets.
The document presents an overview of selection sort, including its definition, algorithm, example, advantages, and disadvantages. Selection sort works by iteratively finding the minimum element in an unsorted sublist and exchanging it with the first element. It has a time complexity of O(n2) but performs well on small lists since it is an in-place sorting algorithm with minimal additional storage requirements. However, it is not efficient for huge datasets due to its quadratic time complexity.
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.
The document discusses sorting algorithms. It begins by defining sorting as arranging data in logical order based on a key. It then discusses internal and external sorting methods. For internal sorting, all data fits in memory, while external sorting handles data too large for memory. The document covers stability, efficiency, and time complexity of various sorting algorithms like bubble sort, selection sort, insertion sort, and merge sort. Merge sort uses a divide-and-conquer approach to sort arrays with a time complexity of O(n log n).
The document discusses Big O notation, which is used to classify algorithms based on how their running time scales with input size. It provides examples of common Big O notations like O(1), O(log n), O(n), O(n^2), and O(n!). The document also explains that Big O looks only at the fastest growing term as input size increases. Well-chosen data structures can help reduce an algorithm's Big O complexity. For example, searching a sorted list is O(log n) rather than O(n) for an unsorted list.
Insertion sort is a sorting algorithm that works by building a sorted array (or list) one item at a time. It maintains two groups, a sorted group and an unsorted group. It removes one element from the unsorted group, finds the location it belongs within the sorted group, and inserts it there. This continues until the unsorted group is empty, leaving a fully sorted list. The worst-case running time is O(n^2) as each insertion may require traversing the entire sorted portion. However, the average case is O(n) for nearly sorted data. A lower bound analysis shows that any algorithm relying on adjacent swaps has a worst case lower bound of Ω(n^2).
Binary Search - Design & Analysis of AlgorithmsDrishti Bhalla
Binary search is an efficient algorithm for finding a target value within a sorted array. It works by repeatedly dividing the search range in half and checking the value at the midpoint. This eliminates about half of the remaining candidates in each step. The maximum number of comparisons needed is log n, where n is the number of elements. This makes binary search faster than linear search, which requires checking every element. The algorithm works by first finding the middle element, then checking if it matches the target. If not, it recursively searches either the lower or upper half depending on if the target is less than or greater than the middle element.
This document presents selection sort, an in-place comparison sorting algorithm. It works by dividing the list into a sorted part on the left and unsorted part on the right. It iterates through the list, finding the smallest element in the unsorted section and swapping it into place. This process continues until the list is fully sorted. Selection sort has a time complexity of O(n^2) in all cases. While it requires no extra storage, it is inefficient for large lists compared to other algorithms.
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.
Branch and Bound is a state space search algorithm that involves generating all children of a node before exploring any children. It uses lower bounds to prune parts of the search tree that cannot produce better solutions than what has already been found. The algorithm is demonstrated on problems like the 8-puzzle and Travelling Salesman Problem. For TSP, it works by reducing the cost matrix at each node to calculate lower bounds, and exploring the child with the lowest estimated total cost.
- Recurrences describe functions in terms of their values on smaller inputs and arise when algorithms contain recursive calls to themselves.
- To analyze the running time of recursive algorithms, the recurrence must be solved to find an explicit formula or bound the expression in terms of n.
- Examples of recurrences and their solutions are given, including binary search (O(log n)), dividing the input in half at each step (O(n)), and dividing the input in half but examining all items (O(n)).
- Methods for solving recurrences include iteration, substitution, and using recursion trees to "guess" the solution.
The document discusses the divide and conquer algorithm design technique. It begins by explaining the basic approach of divide and conquer which is to (1) divide the problem into subproblems, (2) conquer the subproblems by solving them recursively, and (3) combine the solutions to the subproblems into a solution for the original problem. It then provides merge sort as a specific example of a divide and conquer algorithm for sorting a sequence. It explains that merge sort divides the sequence in half recursively until individual elements remain, then combines the sorted halves back together to produce the fully sorted sequence.
This document provides an introduction to asymptotic analysis of algorithms. It discusses analyzing algorithms based on how their running time increases with the size of the input problem. The key points are:
- Algorithms are compared based on their asymptotic running time as the input size increases, which is more useful than actual running times on a specific computer.
- The main types of analysis are worst-case, best-case, and average-case running times.
- Asymptotic notations like Big-O, Omega, and Theta are used to classify algorithms based on their rate of growth as the input increases.
- Common orders of growth include constant, logarithmic, linear, quadratic, and exponential time.
LR parsing is a powerful shift-reduce parsing technique that is both efficient and handles a wide range of grammars. LR parsers work by maintaining a stack and scanning input from left to right. They determine the next action to take by consulting parsing tables generated from the grammar. LR parsing avoids backtracking and can detect syntactic errors as early as possible. The most general form of LR parsing is LR(k) which uses k tokens of lookahead, while practical parsers often use LR(1) or variants like LALR(1) which have smaller and more efficiently constructed tables.
This document discusses analyzing algorithms and asymptotic notation. It defines running time as the number of primitive operations before termination. Examples are provided to illustrate calculating running time functions and classifying them by order of growth such as constant, logarithmic, linear, quadratic, and exponential time. Asymptotic notation such as Big-O, Big-Omega, and Big-Theta are introduced to classify functions by their asymptotic growth rates. Examples are given to demonstrate determining tight asymptotic bounds between functions. Recurrences are defined as equations describing functions in terms of smaller inputs and base cases, which are useful for analyzing recurrent algorithms.
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.
This document discusses the maximum flow and minimum cut problems in networks. It begins by defining key terms like flow, cut, source and sink nodes. It then presents the max-flow min-cut theorem, which states that the maximum flow in a network equals the minimum cut. The document provides examples and proofs of this theorem. It also discusses algorithms for finding the maximum flow in polynomial time and analyzes their running time complexity. The applications of these network flow problems in various domains are also briefly mentioned.
1. Several sorting algorithms are compared including quicksort, heapsort, mergesort, insertion sort, selection sort, and bubble sort.
2. For most algorithms, the best, average, and worst case time complexities are listed as O(n log(n)), O(n log(n)), and O(n^2) respectively except for some cases like selection sort and bubble sort that have average and worst case complexities of O(n^2).
3. For space complexity, many algorithms have worst case complexities of O(n) except for mergesort which uses additional space and has a worst case complexity of O(n).
Selection sort is an algorithm that sorts a list of elements by repeatedly finding the minimum element from the unsorted part of the list and swapping it with the element in the current position. It does this by iterating through the list from beginning to end, selecting the minimum element at each iteration and swapping it into place. The time complexity of selection sort is O(n^2) in both the average and worst cases, making it inefficient for large lists.
this is a briefer overview about the Big O Notation. Big O Notaion are useful to check the Effeciency of an algorithm and to check its limitation at higher value. with big o notation some examples are also shown about its cases and some functions in c++ are also described.
The document discusses disjoint sets and operations on disjoint sets such as union and find. Disjoint sets are sets that do not have any common elements. The union of two disjoint sets combines all the elements of both sets. The find operation takes an element as input and returns the set that contains that element. Disjoint sets can be represented using a tree structure. Algorithms for union and find operations are presented, including weighted union and collapsing find techniques that improve the efficiency.
Selection sort is a sorting algorithm that finds the smallest element in an unsorted list and swaps it with the first element, then finds the next smallest element and swaps it with the second element, continuing in this way until the list is fully sorted. It works by iterating through the list, finding the minimum element, and swapping it into its correct place at each step.
This document provides an overview of natural language processing and planning topics including:
- NLP tasks like parsing, machine translation, and information extraction.
- The components of a planning system including the planning agent, state and goal representations, and planning techniques like forward and backward chaining.
- Methods for natural language processing including pattern matching, syntactic analysis, and the stages of NLP like phonological, morphological, syntactic, semantic, and pragmatic analysis.
This presentation focus on the optimization problem-solving method i.e. greedy method. It also included a basic definition, components of the algorithm, effective steps, general algorithm, and applications.
This document discusses functions in discrete mathematical structures. It defines a function as mapping elements from one set to unique elements in another set. A function assigns a single element from the codomain to each element in the domain. An example of a string length function maps strings to their lengths. The document also defines related terms like domain, codomain, image, and pre-image. It provides an example of a grade function and asks the reader to identify the domain, codomain, and range based on given information. Finally, it concludes with discussing functions and provides references for further reading.
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.
Branch and Bound is a state space search algorithm that involves generating all children of a node before exploring any children. It uses lower bounds to prune parts of the search tree that cannot produce better solutions than what has already been found. The algorithm is demonstrated on problems like the 8-puzzle and Travelling Salesman Problem. For TSP, it works by reducing the cost matrix at each node to calculate lower bounds, and exploring the child with the lowest estimated total cost.
- Recurrences describe functions in terms of their values on smaller inputs and arise when algorithms contain recursive calls to themselves.
- To analyze the running time of recursive algorithms, the recurrence must be solved to find an explicit formula or bound the expression in terms of n.
- Examples of recurrences and their solutions are given, including binary search (O(log n)), dividing the input in half at each step (O(n)), and dividing the input in half but examining all items (O(n)).
- Methods for solving recurrences include iteration, substitution, and using recursion trees to "guess" the solution.
The document discusses the divide and conquer algorithm design technique. It begins by explaining the basic approach of divide and conquer which is to (1) divide the problem into subproblems, (2) conquer the subproblems by solving them recursively, and (3) combine the solutions to the subproblems into a solution for the original problem. It then provides merge sort as a specific example of a divide and conquer algorithm for sorting a sequence. It explains that merge sort divides the sequence in half recursively until individual elements remain, then combines the sorted halves back together to produce the fully sorted sequence.
This document provides an introduction to asymptotic analysis of algorithms. It discusses analyzing algorithms based on how their running time increases with the size of the input problem. The key points are:
- Algorithms are compared based on their asymptotic running time as the input size increases, which is more useful than actual running times on a specific computer.
- The main types of analysis are worst-case, best-case, and average-case running times.
- Asymptotic notations like Big-O, Omega, and Theta are used to classify algorithms based on their rate of growth as the input increases.
- Common orders of growth include constant, logarithmic, linear, quadratic, and exponential time.
LR parsing is a powerful shift-reduce parsing technique that is both efficient and handles a wide range of grammars. LR parsers work by maintaining a stack and scanning input from left to right. They determine the next action to take by consulting parsing tables generated from the grammar. LR parsing avoids backtracking and can detect syntactic errors as early as possible. The most general form of LR parsing is LR(k) which uses k tokens of lookahead, while practical parsers often use LR(1) or variants like LALR(1) which have smaller and more efficiently constructed tables.
This document discusses analyzing algorithms and asymptotic notation. It defines running time as the number of primitive operations before termination. Examples are provided to illustrate calculating running time functions and classifying them by order of growth such as constant, logarithmic, linear, quadratic, and exponential time. Asymptotic notation such as Big-O, Big-Omega, and Big-Theta are introduced to classify functions by their asymptotic growth rates. Examples are given to demonstrate determining tight asymptotic bounds between functions. Recurrences are defined as equations describing functions in terms of smaller inputs and base cases, which are useful for analyzing recurrent algorithms.
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.
This document discusses the maximum flow and minimum cut problems in networks. It begins by defining key terms like flow, cut, source and sink nodes. It then presents the max-flow min-cut theorem, which states that the maximum flow in a network equals the minimum cut. The document provides examples and proofs of this theorem. It also discusses algorithms for finding the maximum flow in polynomial time and analyzes their running time complexity. The applications of these network flow problems in various domains are also briefly mentioned.
1. Several sorting algorithms are compared including quicksort, heapsort, mergesort, insertion sort, selection sort, and bubble sort.
2. For most algorithms, the best, average, and worst case time complexities are listed as O(n log(n)), O(n log(n)), and O(n^2) respectively except for some cases like selection sort and bubble sort that have average and worst case complexities of O(n^2).
3. For space complexity, many algorithms have worst case complexities of O(n) except for mergesort which uses additional space and has a worst case complexity of O(n).
Selection sort is an algorithm that sorts a list of elements by repeatedly finding the minimum element from the unsorted part of the list and swapping it with the element in the current position. It does this by iterating through the list from beginning to end, selecting the minimum element at each iteration and swapping it into place. The time complexity of selection sort is O(n^2) in both the average and worst cases, making it inefficient for large lists.
this is a briefer overview about the Big O Notation. Big O Notaion are useful to check the Effeciency of an algorithm and to check its limitation at higher value. with big o notation some examples are also shown about its cases and some functions in c++ are also described.
The document discusses disjoint sets and operations on disjoint sets such as union and find. Disjoint sets are sets that do not have any common elements. The union of two disjoint sets combines all the elements of both sets. The find operation takes an element as input and returns the set that contains that element. Disjoint sets can be represented using a tree structure. Algorithms for union and find operations are presented, including weighted union and collapsing find techniques that improve the efficiency.
Selection sort is a sorting algorithm that finds the smallest element in an unsorted list and swaps it with the first element, then finds the next smallest element and swaps it with the second element, continuing in this way until the list is fully sorted. It works by iterating through the list, finding the minimum element, and swapping it into its correct place at each step.
This document provides an overview of natural language processing and planning topics including:
- NLP tasks like parsing, machine translation, and information extraction.
- The components of a planning system including the planning agent, state and goal representations, and planning techniques like forward and backward chaining.
- Methods for natural language processing including pattern matching, syntactic analysis, and the stages of NLP like phonological, morphological, syntactic, semantic, and pragmatic analysis.
This presentation focus on the optimization problem-solving method i.e. greedy method. It also included a basic definition, components of the algorithm, effective steps, general algorithm, and applications.
This document discusses functions in discrete mathematical structures. It defines a function as mapping elements from one set to unique elements in another set. A function assigns a single element from the codomain to each element in the domain. An example of a string length function maps strings to their lengths. The document also defines related terms like domain, codomain, image, and pre-image. It provides an example of a grade function and asks the reader to identify the domain, codomain, and range based on given information. Finally, it concludes with discussing functions and provides references for further reading.
9. Searching & Sorting - Data Structures using C++ by Varsha Patilwidespreadpromotion
The document discusses various searching and sorting algorithms. It covers sequential search, binary search, Fibonacci search, hashed search, indexed sequential search and their time complexities. Sorting algorithms like bubble sort, insertion sort, selection sort are explained along with their analysis. Internal sorting techniques like quicksort, heapsort, radix sort and bucket sort are also mentioned. The document provides details on sorting methods, order, stability and efficiency.
This document discusses looping structures in algorithms and programming. It defines looping as repeating statements to fulfill a looping condition. The main types of looping structures are for, while, and repeat loops. Examples are given in pseudocode and Pascal to illustrate for loops that count ascending and descending, while loops, and repeat loops. Exercises are provided to practice different types of loops.
Esta dinâmica celebra a libertação simbolizando a escravidão de uma pessoa amarrada. Os participantes são convidados a remover as amarras uma a uma, expressando o que desejam libertar, enquanto cantos sobre escravidão são tocados. No final, a pessoa libertada compartilha seus sentimentos e o grupo reflete sobre como ajudar os outros na luta contra formas modernas de escravidão.
Mid Level Counterintelligence Support Specialist - AfghanistanAngelene Green
Mid Level Counterintelligence Support Specialist - Afghanistan
Veterans should login or register at www.casy.us, Click on the Job Seeker tab and search for: Req: 180263BR
Algorithm and Programming (Introduction of dev pascal, data type, value, and ...Adam Mukharil Bachtiar
This file contains explanation about introduction of dev pascal, data type, value, and identifier. This file was used in my Algorithm and Programming Class.
Bubble sort adalah algoritma pengurutan yang mengurutkan elemen array dengan membandingkan dan menukar posisi elemen yang berurutan jika diperlukan, proses ini dilakukan berulang hingga seluruh elemen terurut. Bubble sort merupakan metode pengurutan paling sederhana namun lambat dibanding jenis pengurutan lain.
certificate in health and safety level 2Luca De Rosa
Luca De Rosa was issued certificate number 5683132 on July 13, 2016 for completing Health and Safety Level 2. The certificate was for successfully completing a health and safety course and achieving level 2 qualifications.
La población de Bucaramanga ha crecido a un ritmo promedio del 0.98% en los últimos 25 años, expandiéndose principalmente hacia el norte. Este crecimiento ha generado más basura y contaminación por vehículos, pero también ha impulsado el desarrollo industrial, con más de 400 millones de dólares invertidos recientemente. A menos que se mejore la gestión de residuos y medio ambiente, para los próximos 50 años Bucaramanga corre el riesgo de convertirse en un basurero en lugar de aprovechar su potencial de
Chapter 8 advanced sorting and hashing for printAbdii Rashid
Shell sort improves on insertion sort by first sorting elements that are already partially sorted. It does this by using a sequence of increment values to sort sublists within the main list. The time complexity of shell sort is O(n^3/2).
Quicksort uses a divide and conquer approach. It chooses a pivot element and partitions the list into two sublists based on element values relative to the pivot. The sublists are then recursively sorted. The average time complexity of quicksort is O(nlogn) but it can be O(n^2) in the worst case.
Mergesort follows the same divide and conquer strategy as quicksort. It recursively divides the list into halves until single elements
Sorting and Searching is one of the most vital topics in DSA. Storing and retrieving information is one of the most common applications of computers nowadays. According to time the amount of data and information stored and accessed via computer has turned to huge databases. So many techniques and algorithms have been developed to efficiently maintain and process information in databases. The process of looking up a particular data record in the database is called searching. The process of ordering the records in a database is called Sorting. Sorting and searching together constitute a major area of study in computational methods. Both of them are very important fields of study in data structure and algorithms. Let us discuss both the topics in detail here.
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion should be inserted
while some elements unsorted:
Using linear search, find the location in the sorted portion where the 1st element of the unsorted portion
This document provides information about sorting algorithms. It begins with an introduction to sorting, explaining why sorting is important and common sorting algorithms like merge sort, quicksort, heapsort, etc. It then discusses different types of sorting algorithms like comparison-based sorting and specialized sorting. The document proceeds to explain several specific sorting algorithms in detail, including bubble sort, selection sort, insertion sort, merge sort, and bitonic sort. It provides pseudocode for the algorithms and examples to illustrate how they work on sample data. The key details covered are the time complexity of common sorting algorithms and how different algorithms have tradeoffs in terms of speed and memory usage.
The document discusses various sorting algorithms. It begins by defining a sorting algorithm as arranging elements of a list in a certain order, such as numerical or alphabetical order. It then discusses popular sorting algorithms like insertion sort, bubble sort, merge sort, quicksort, selection sort, and heap sort. For each algorithm, it provides examples to illustrate how the algorithm works step-by-step to sort a list of numbers. Code snippets are also included for insertion sort and bubble sort.
This document describes and compares several common sorting algorithms, including bubble sort, selection sort, and insertion sort. It provides pseudocode examples to illustrate how each algorithm works and analyzes their time complexities. Specifically, it shows the steps to sort sample data using each algorithm through multiple iterations and compares their performance, with bubble, selection, and insertion sorts having O(n2) time and others like merge and quicksort having O(n log n) time.
This document discusses sorting algorithms. It begins by defining sorting as arranging items in a sequence. It notes that 25-50% of computing power is used for sorting activities. Common sorting applications include organizing lists of student data, test scores, and race results. Sorting methods described include selection sort, bubble sort, shell sort, and quick sort. Selection sort works by repeatedly finding the largest element and swapping it into the sorted portion of the array. Bubble sort compares adjacent elements and swaps them if out of order, pushing larger elements towards the end over multiple passes. Pseudocode and C++ code examples are provided to demonstrate how selection and bubble sort algorithms work on integer and string arrays.
This document discusses sorting algorithms. It begins by defining sorting as arranging items in a sequence. Approximately 25-50% of computing power is used for sorting activities. Common sorting applications include organizing student data, scores before grading, and race results for payouts. Selection sort and bubble sort algorithms are presented in detail, with pseudocode and examples. Selection sort finds the largest element and moves it to the end of the unsorted portion each pass. Bubble sort compares adjacent elements and swaps any out of order until the list is fully sorted. Both can sort arrays of integers or strings.
The document discusses various searching and sorting techniques. It describes linear and binary searching algorithms. It also explains different sorting algorithms like bubble sort, insertion sort, selection sort, and quick sort. Their time complexities and applications are discussed.
The document discusses various sorting algorithms. It provides an overview of bubble sort, selection sort, and insertion sort. For bubble sort, it explains the process of "bubbling up" the largest element to the end of the array through successive passes. For selection sort, it illustrates the process of finding the largest element and swapping it into the correct position in each pass to sort the array. For insertion sort, it notes that elements are inserted into the sorted portion of the array in the proper position.
one main advantage of bubble sort as compared to othersAjay Chimmani
Bubble sort is a simple sorting algorithm that compares adjacent elements and swaps them if they are in the wrong order. It repeats this process, making multiple passes through the list, until it is fully sorted. While simple to implement, bubble sort has a poor worst-case performance of O(n2) time complexity, making it impractical for large datasets. It is often used to introduce sorting concepts but not recommended for real-world use due to its inefficient runtime.
The document provides an overview of algorithms and pseudocode. It discusses key algorithm concepts like searching, sorting, and optimization problems. Specific searching algorithms covered are linear search and binary search, while sorting algorithms discussed include bubble sort and insertion sort. Pseudocode is introduced as a way to describe algorithms using plain language instead of a programming syntax. Examples are provided to demonstrate linear search, binary search, and bubble sort algorithms. The document aims to teach the fundamentals of algorithms through examples and explanations of common algorithm problems and solutions.
All types of Sorting logic.Some algorithms (selection, bubble, heapsort) work by moving elements to their final position, one at a time. You sort an array of size N, put 1 item in place, and continue sorting an array of size N – 1 (heapsort is slightly different).
Some algorithms (insertion, quicksort, counting, radix) put items into a temporary position, close(r) to their final position. You rescan, moving items closer to the final position with each iteration.
One technique is to start with a “sorted list” of one element, and merge unsorted items into it, one at a time.
The document discusses stacks and their properties. It defines a stack as a linear data structure where items are inserted and deleted from one end, following the LIFO (last in, first out) principle. Stacks are commonly used in computer systems for tasks like compiler operations and process scheduling. The key stack operations are push to insert an item and pop to remove the top item.
The document discusses various sorting algorithms including exchange sorts like bubble sort and quicksort, selection sorts like straight selection sort, and tree sorts like heap sort. For each algorithm, it provides an overview of the approach, pseudocode, analysis of time complexity, and examples. Key algorithms covered are bubble sort (O(n2)), quicksort (average O(n log n)), selection sort (O(n2)), and heap sort (O(n log n)).
The document discusses algorithm analysis and design. It begins with an introduction to analyzing algorithms including average case analysis and solving recurrences. It then provides definitions of algorithms both informal and formal. Key aspects of algorithm study and specification methods like pseudocode are outlined. Selection sort and tower of Hanoi problems are presented as examples and analyzed for time and space complexity. Average case analysis is discussed assuming all inputs are equally likely.
Sorting in data structures and algorithms , it has all the necessary points t...BhumikaBiyani1
1) The document discusses various sorting algorithms like bubble sort, selection sort, insertion sort, merge sort, quick sort and heap sort.
2) It provides detailed explanations of how merge sort and quick sort algorithms work, including examples with diagrams showing the sorting process step-by-step.
3) Merge sort has a time complexity of O(n log n) as it recursively divides the array into halves and then merges the sorted halves, while quick sort selects a pivot element and partitions the array into elements less than and greater than the pivot in O(n) time on average.
Dokumen tersebut memberikan tips untuk membuat formatting kode program yang baik agar mudah dibaca dan dipahami. Terdapat dua jenis formatting, yaitu vertical dan horizontal formatting. Secara vertical, kode perlu diatur dengan memperhatikan konsep-konsep, jarak antar konsep, kerapatan kode yang berkaitan, dan letak deklarasi dan pemanggilan fungsi. Secara horizontal, perlu memperhatikan pemberian jarak, penyamaan baris, dan pengindentasian untuk membedakan struktur program.
Slide ini menjelaskan perihal penggunaan komentar yang baik dan buruk pada suatu kode program. Slide ini merupakan bahan ajar untuk mata kuliah Clean Code dan Design Pattern.
Dokumen tersebut memberikan tips-tips untuk membuat nama variabel, fungsi, kelas, dan paket yang baik dalam pembuatan kode program. Beberapa tips utama adalah menggunakan nama yang jelas maksudnya, hindari penggunaan encoding, gunakan kata benda untuk nama kelas dan verba untuk nama metode, serta tambahkan konteks yang bermakna.
Dokumen tersebut membahas tentang pengujian perangkat lunak, termasuk definisi pengujian perangkat lunak, tujuan pengujian, jenis pengujian seperti manual testing, automated testing, unit testing, integration testing, serta metode pengujian seperti white box testing dan black box testing.
Slide ini berisi penjelasan tentang Data Mining Klasifikasi. Di dalamnya ada tiga algoritma yang dibahas, yaitu: Naive Bayes, kNN, dan ID3 (Decision Tree).
Dokumen tersebut membahas algoritma program dinamis untuk menentukan lintasan terpendek antara dua simpul dalam sebuah graf. Metode yang digunakan adalah program dinamis mundur dimana permasalahan dibagi menjadi beberapa tahap dan dihitung secara mundur untuk menentukan nilai optimal pada setiap tahap. Hasil akhir adalah terdapat tiga lintasan terpendek dengan panjang 11 antara simpul 1 dan 10.
Teks tersebut membahas strategi algoritma Divide and Conquer untuk memecahkan masalah. Strategi ini membagi masalah menjadi submasalah kecil, memecahkan submasalah tersebut secara rekursif, lalu menggabungkan hasilnya untuk mendapatkan solusi masalah awal. Dua contoh masalah yang dijelaskan adalah mencari nilai maksimum dan minimum dalam tabel, serta mencari pasangan titik terdekat dalam himpunan titik.
Slide ini berisi penjelasan tentang teorema-teorema yang berlaku untuk notasi asimptotik beserta cara perhitungannya untuk kebutuhan waktu suatu algoritma.
🌱 Green Grafana 🌱 Essentials_ Data, Visualizations and Plugins.pdfImma Valls Bernaus
eady to harness the power of Grafana for your HackUPC project? This session provides a rapid introduction to the core concepts you need to get started. We'll cover Grafana fundamentals and guide you through the initial steps of building both compelling dashboards and your very first Grafana app. Equip yourself with the essential tools to visualize your data and bring your innovative ideas to life!
AgentExchange is Salesforce’s latest innovation, expanding upon the foundation of AppExchange by offering a centralized marketplace for AI-powered digital labor. Designed for Agentblazers, developers, and Salesforce admins, this platform enables the rapid development and deployment of AI agents across industries.
Email: [email protected]
Phone: +1(630) 349 2411
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AI in Business Software: Smarter Systems or Hidden Risks?Amara Nielson
AI in Business Software: Smarter Systems or Hidden Risks?
Description:
This presentation explores how Artificial Intelligence (AI) is transforming business software across CRM, HR, accounting, marketing, and customer support. Learn how AI works behind the scenes, where it’s being used, and how it helps automate tasks, save time, and improve decision-making.
We also address common concerns like job loss, data privacy, and AI bias—separating myth from reality. With real-world examples like Salesforce, FreshBooks, and BambooHR, this deck is perfect for professionals, students, and business leaders who want to understand AI without technical jargon.
✅ Topics Covered:
What is AI and how it works
AI in CRM, HR, finance, support & marketing tools
Common fears about AI
Myths vs. facts
Is AI really safe?
Pros, cons & future trends
Business tips for responsible AI adoption
Join Ajay Sarpal and Miray Vu to learn about key Marketo Engage enhancements. Discover improved in-app Salesforce CRM connector statistics for easy monitoring of sync health and throughput. Explore new Salesforce CRM Synch Dashboards providing up-to-date insights into weekly activity usage, thresholds, and limits with drill-down capabilities. Learn about proactive notifications for both Salesforce CRM sync and product usage overages. Get an update on improved Salesforce CRM synch scale and reliability coming in Q2 2025.
Key Takeaways:
Improved Salesforce CRM User Experience: Learn how self-service visibility enhances satisfaction.
Utilize Salesforce CRM Synch Dashboards: Explore real-time weekly activity data.
Monitor Performance Against Limits: See threshold limits for each product level.
Get Usage Over-Limit Alerts: Receive notifications for exceeding thresholds.
Learn About Improved Salesforce CRM Scale: Understand upcoming cloud-based incremental sync.
Meet the New Kid in the Sandbox - Integrating Visualization with PrometheusEric D. Schabell
When you jump in the CNCF Sandbox you will meet the new kid, a visualization and dashboards project called Perses. This session will provide attendees with the basics to get started with integrating Prometheus, PromQL, and more with Perses. A journey will be taken from zero to beautiful visualizations seamlessly integrated with Prometheus. This session leaves the attendees with hands-on self-paced workshop content to head home and dive right into creating their first visualizations and integrations with Prometheus and Perses!
Perses (visualization) - Great observability is impossible without great visualization! Learn how to adopt truly open visualization by installing Perses, exploring the provided tooling, tinkering with its API, and then get your hands dirty building your first dashboard in no time! The workshop is self-paced and available online, so attendees can continue to explore after the event: https://o11y-workshops.gitlab.io/workshop-perses
Microsoft AI Nonprofit Use Cases and Live Demo_2025.04.30.pdfTechSoup
In this webinar we will dive into the essentials of generative AI, address key AI concerns, and demonstrate how nonprofits can benefit from using Microsoft’s AI assistant, Copilot, to achieve their goals.
This event series to help nonprofits obtain Copilot skills is made possible by generous support from Microsoft.
What You’ll Learn in Part 2:
Explore real-world nonprofit use cases and success stories.
Participate in live demonstrations and a hands-on activity to see how you can use Microsoft 365 Copilot in your own work!
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A Deep Dive into Odoo CRM: Lead Management, Automation & MoreSatishKumar2651
This presentation covers the key features of Odoo CRM, including lead management, pipeline tracking, email/VoIP integration, reporting, and automation. Ideal for businesses looking to streamline sales with an open-source, scalable CRM solution.
Explore this in-depth presentation covering the core features of Odoo CRM, one of the most flexible and powerful open-source CRM solutions for modern businesses.
This deck includes:
✅ Lead & Pipeline Management
✅ Activity Scheduling
✅ Email & VoIP Integration
✅ Real-Time Reporting
✅ Workflow Automation
✅ Multi-channel Lead Capture
✅ Integration with other Odoo modules
Whether you're in manufacturing, services, real estate, or healthcare—Odoo CRM can help you streamline your sales process and boost conversions.
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Who Watches the Watchmen (SciFiDevCon 2025)Allon Mureinik
Tests, especially unit tests, are the developers’ superheroes. They allow us to mess around with our code and keep us safe.
We often trust them with the safety of our codebase, but how do we know that we should? How do we know that this trust is well-deserved?
Enter mutation testing – by intentionally injecting harmful mutations into our code and seeing if they are caught by the tests, we can evaluate the quality of the safety net they provide. By watching the watchmen, we can make sure our tests really protect us, and we aren’t just green-washing our IDEs to a false sense of security.
Talk from SciFiDevCon 2025
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Best Accounting Practice Management Software Guide for 2025Tidyflow
This slide deck, “Best Accounting Practice Management Software Guide for 2025,” is designed for solo CPAs, small accounting firms, and growing mid-sized practices looking to navigate the crowded and sometimes overwhelming world of accounting practice management tools.
In 2025, the pressure on accounting firms is higher than ever. Clients expect fast, clear communication. Teams are often working remotely or in hybrid setups. Deadlines are tight, and regulations are constantly changing. Choosing the right practice management software can make all the difference — helping firms stay organized, automate repetitive work, collaborate smoothly, and ultimately deliver better service to clients.
This presentation offers a clear, unbiased comparison of seven of the most popular practice management platforms used by accounting firms today, including:
Tidyflow — a lean, no-bloat tool built for small and growing firms;
Karbon — a powerful platform with integrated email and advanced team workflows;
TaxDome — a robust system designed especially for tax-focused firms;
Canopy — a modular, flexible solution for firms that want to mix and match tools;
Financial Cents — a simple, budget-friendly option for small firms;
Jetpack Workflow — a lightweight workflow tracker great for solo practitioners and small teams;
Pixie — a client-focused platform offering unlimited users at a flat price.
Each software is broken down with key details on pricing, best-fit firm type, notable features, integrations, pros, and cons. We’ve also included a quick comparison table upfront to help busy practitioners scan for top-line differences.
What you’ll learn from this slide deck:
✅ Which tools are best suited for your firm’s size and growth stage
✅ What features (client portals, time tracking, automation, integrations) matter most
✅ How pricing models compare across popular providers
✅ Where each software excels — and where it might fall short
The final slides offer guidance on how to choose the right software for your unique needs, with a checklist covering firm size, required features, ease of use, integrations, and budget considerations.
Whether you’re looking for an affordable, scalable option like Tidyflow or a powerful enterprise-level solution like Karbon, this guide will help you make a more informed, confident decision for 2025 and beyond.
For more details, visit tidyflow.com — a modern practice management platform designed specifically for small and growing accounting firms that want a simple, affordable solution without enterprise-level complexity.
Top 10 Data Cleansing Tools for 2025.pdfAffinityCore
Discover the top 10 data cleansing tools for 2025, designed to help businesses clean, transform, and enhance data accuracy. Improve decision-making and data quality with these powerful solutions.
7. WhatisBubbleSort
• Sorting algorithm which was inspired by
bubble soap.
• Comparing element of array (i) with next
element of it (i+1).
• If i is bigger than i+1 then swap value of
each element.
20. Process of Maximum Sort (Ascending)
This is an array that will be sorted in Ascending way :
6 3 9 1 5
Step 1 : 6 3 9 1 5
6 3 9 1 5
6 3 9 1 5
6 3 9 1 5
6 3 9 1 5
6 3 5 1 9
j
j
j
jmax
max
max
max
max j
21. Process of Maximum Sort (Ascending)
Step 2 : 6 3 5 1 9
6 3 5 1 9
6 3 5 1 9
6 3 5 1 9
1 3 5 6 9
j
j
jmax
max
max
max j
22. Process of Maximum Sort (Ascending)
Step 3 : 1 3 5 6 9
1 3 5 6 9
1 3 5 6 9
1 3 5 6 9
Step 4 : 1 3 5 6 9
1 3 5 6 9
Array after sorted in descending way:
1 3 5 6 9
j
j
max
max
max
max
j
j
max
23. General Format for Maximum Sort Ascending
1
2
3
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Procedure MaximumSortAsc(I/O nama_var_array : nama_tipe_array,
Input N : integer)
{I.S. : array [1..N] sudah terdefinisi}
{F.S. : menghasilkan array [1..N] yang tersusun secara ascending}
Kamus:
i, j, max, x : integer
temp : tipedata
Algoritma:
x n
for i 1 to N-1 do
max 1
for j 2 to x do
if(nama_var_array[j] > nama_var_array[max])
then
max j
endif
endfor
temp nama_var_array[max]
nama_var_array[max] nama_var_array[j]
nama_var_array[j] temp
x x - 1
endfor
EndProcedure
24. General Format for Minimum Sort Ascending
1
2
3
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5
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9
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18
19
20
Procedure MinimumSortAsc(I/O nama_var_array : nama_tipe_array,
Input N : integer)
{I.S. : array[1..n] sudah terdefinisi}
{F.S. : menghasilkan array [1..n] yang tersusun secara ascending}
Kamus:
i, j, min : integer
temp : tipedata
Algoritma:
for i 1 to (N – 1) do
min i
for j i+1 to N do
if(nama_var_array[j] < nama_var_array[min])
then
min j
endif
endfor
temp nama_var_array[min]
nama_var_array[min] nama_var_array[i]
nama_var_array[i] temp
endfor
EndProcedure