String literals in python are surrounded by either single quotation marks, or double quotation marks. Strings can be output to screen using the print function. For example: print("hello"). Like many other popular programming languages, strings in Python are arrays of bytes representing unicode characters.
This document discusses priority queues. It defines a priority queue as a queue where insertion and deletion are based on some priority property. Items with higher priority are removed before lower priority items. There are two main types: ascending priority queues remove the smallest item, while descending priority queues remove the largest item. Priority queues are useful for scheduling jobs in operating systems, where real-time jobs have highest priority and are scheduled first. They are also used in network communication to manage limited bandwidth.
NumPy is a library for working with multidimensional arrays and matrices in Python. It allows mathematical and logical operations on arrays to be performed. This tutorial explains the basics of NumPy, including its architecture, data types, array attributes, array creation, indexing and slicing, broadcasting, and array manipulation functions. The audience is those looking to learn the basics of NumPy, which is useful for algorithm developers. A basic understanding of Python is recommended.
The linear search algorithm involves checking all elements of an array or data structure sequentially until the target element is found. In the worst case, all elements must be checked, resulting in O(n) time complexity where n is the number of elements. However, if the target is the first element, it requires only constant O(1) time. The algorithm is simple to implement but does not scale well to large data sets as the search time grows linearly with the number of elements.
Selection sort is a sorting algorithm that works by repeatedly finding the minimum element from an unsorted sublist and putting it at the front. It has the following steps:
1. Start with an unsorted list.
2. Find the minimum element in the list and swap it with the first element.
3. Repeat step 2 for the remaining elements, each time considering one less element at the front that is already in sorted order.
The algorithm runs in O(n2) time as the inner loop needs to run n-1 times for n elements. It works for both ascending and descending order by changing the comparison operator in the inner loop.
NumPy is a Python library used for working with multi-dimensional arrays and matrices for scientific computing. It allows fast operations on large data sets and arrays. NumPy arrays can be created from lists or ranges of values and support element-wise operations via universal functions. NumPy is the foundation of the Python scientific computing stack and provides key features like broadcasting for efficient computations.
Queue is a first-in first-out (FIFO) data structure where elements can only be added to the rear of the queue and removed from the front of the queue. It has two pointers - a front pointer pointing to the front element and a rear pointer pointing to the rear element. Queues can be implemented using arrays or linked lists. Common queue operations include initialization, checking if empty/full, enqueue to add an element, and dequeue to remove an element. The document then describes how these operations work for queues implemented using arrays, linked lists, and circular arrays. It concludes by providing exercises to implement specific queue tasks.
1. Linear search is a method for finding a particular value in a list that checks each element in sequence until the desired element is found or the list is exhausted.
2. The best case for linear search is O(1) when the target is found at the first location. The worst case is O(n) when the target is at the end or not present.
3. The average time complexity of linear search is O(n) as the target has an equal chance of being in any position, so on average half the list must be searched.
This document provides an overview of selection sort, including its objectives, previous knowledge required, algorithm, program, animation, applications, and advantages/disadvantages. Selection sort works by iterating through an array and swapping the smallest remaining element into the sorted portion of the array. It is useful for sorting small arrays or when memory is limited, but performs more slowly as the number of elements increases compared to other sorting algorithms.
The selection sort algorithm works by iterating through an array, finding the minimum/maximum value, and swapping it into the correct sorted position. It does this by keeping track of the index of the minimum/maximum value found on each pass. The number of passes is equal to the length of the array. In each pass, it finds the minimum/maximum value and swaps it into the current place, sorting the array further.
The document discusses the convex hull algorithm. It begins by defining a convex hull as the shape a rubber band would take if stretched around pins on a board. It then provides explanations of extreme points, edges, and applications of convex hulls. Various algorithms for finding convex hulls are presented, including divide and conquer in O(n log n) time and Jarvis march in O(n^2) time in the worst case.
This document provides information about Python lists. Some key points:
- Lists can store multiple elements of any data type. They are versatile for working with multiple elements.
- Lists maintain element order and allow duplicate elements. Elements are accessed via indexes.
- Lists support operations like concatenation, membership testing, slicing, and methods to add/remove elements.
- Nested lists allow lists within lists, for representing matrices and other complex data structures.
This document discusses various sorting algorithms and their complexities. It begins by defining an algorithm and complexity measures like time and space complexity. It then defines sorting and common sorting algorithms like bubble sort, selection sort, insertion sort, quicksort, and mergesort. For each algorithm, it provides a high-level overview of the approach and time complexity. It also covers sorting algorithm concepts like stable and unstable sorting. The document concludes by discussing future directions for sorting algorithms and their applications.
The document discusses queue data structures. A queue is a linear data structure where additions are made at the end/tail and removals are made from the front/head, following a First-In First-Out (FIFO) approach. Common queue operations include add, remove, check if empty, check if full. A queue can be stored using either a static array or dynamic linked nodes. The key aspects are maintaining references to the head and tail of the queue.
NumPy is a Python library used for working with multidimensional arrays and matrices for scientific computing. It allows fast operations on arrays through optimized C code and is the foundation of the Python scientific computing stack. NumPy arrays can be created in many ways and support operations like indexing, slicing, broadcasting, and universal functions. NumPy provides many useful features for linear algebra, Fourier transforms, random number generation and more.
The document discusses the dynamic programming approach to solving the Fibonacci numbers problem and the rod cutting problem. It explains that dynamic programming formulations first express the problem recursively but then optimize it by storing results of subproblems to avoid recomputing them. This is done either through a top-down recursive approach with memoization or a bottom-up approach by filling a table with solutions to subproblems of increasing size. The document also introduces the matrix chain multiplication problem and how it can be optimized through dynamic programming by considering overlapping subproblems.
The document discusses insertion operations in arrays. It explains that to insert a new element into a one dimensional array, space must be created for the new element by moving existing elements. For example, to insert between the first and second element, the last N-1 elements would need to be moved down. An algorithm for insertion is provided that involves shifting elements after the insertion point over by one index to make space and then inserting the new element at that index.
This document discusses data types in C programming. It describes primitive data types like integers, floats, characters and their syntax. It also covers non-primitive data types like arrays, structures, unions, and linked lists. Arrays store a collection of similar data types, structures group different data types, and unions store different types in the same memory location. Linked lists are dynamic data structures using pointers. The document also provides overviews of stacks and queues, describing their LIFO and FIFO properties respectively.
---TABLE OF CONTENT---
Introduction
Differences between crisp sets & Fuzzy sets
Operations on Fuzzy Sets
Properties
MF formulation and parameterization
Fuzzy rules and Fuzzy reasoning
Fuzzy interface systems
Introduction to genetic algorithm
Analysis of Algorithm (Bubblesort and Quicksort)Flynce Miguel
Quicksort is a recursive divide-and-conquer algorithm that works by selecting a pivot element and partitioning the array into two subarrays of elements less than and greater than the pivot. It recursively sorts the subarrays. The divide step does all the work by partitioning, while the combine step does nothing. It has average case performance of O(n log n) but worst case of O(n^2). Bubble sort repeatedly swaps adjacent elements that are out of order until the array is fully sorted. It has a simple implementation but poor performance of O(n^2).
Design & Analysis of Algorithms Lecture NotesFellowBuddy.com
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The document discusses several sorting algorithms: selection sort, bubble sort, quicksort, and merge sort. Selection sort has linear time complexity for swaps but quadratic time for comparisons. Bubble sort is quadratic time for both swaps and comparisons, making it the least efficient. Quicksort and merge sort are the fastest algorithms, both with logarithmic time complexity of O(n log n) for swaps and comparisons. Quicksort risks quadratic behavior in the worst case if pivots are chosen poorly, while merge sort requires more data copying between temporary and full lists.
Searching and sorting
Types of Searching
1. Linear Searching
2. Binary Searching
Types of Sorting
1.Selection Sort
2. Insertion Sort
3.Bubble Sort
And the examples of Linear searching, Binary Searching
And also the examples of Selection sort, Insertion sort and Bubble sort and describing them in detail in this ppt
This document discusses trees as a non-linear data structure. It defines key tree terminology such as root node, leaf node, and describes different types of trees including binary trees, binary search trees, and expression trees. Binary trees can have at most two children per node and are used in applications like expression evaluation. Binary search trees store elements in sorted order with left subtrees containing smaller elements and right subtrees containing larger elements. Expression trees represent arithmetic expressions with operators as internal nodes and operands as leaf nodes.
This document provides an overview of set theory, including definitions and concepts. It begins by defining a set as a collection of distinct objects, called elements or members. It describes how sets are denoted and provides examples. Key concepts covered include subsets, the empty set, set operations like union and intersection, and properties of sets. The document also discusses topics like the power set, Cartesian products, partitions, and the universal set. Overall, it serves as a comprehensive introduction to the basic ideas and terminology of set theory.
Selection sort is an in-place comparison sorting algorithm where the minimum element from the unsorted section of the list is selected in each pass and swapped with the first element. It has a time complexity of O(n2) making it inefficient for large lists. The algorithm involves dividing the list into sorted and unsorted sublists, finding the minimum element in the unsorted sublist, swapping it with the first element and moving the imaginary wall between the two sublists by one element. This process is repeated for n-1 passes to completely sort an input list of n elements. Pseudocode for the algorithm using a nested for loop to find the minimum element and swap it is also provided.
This document provides an overview of selection sort, including its objectives, previous knowledge required, algorithm, program, animation, applications, and advantages/disadvantages. Selection sort works by iterating through an array and swapping the smallest remaining element into the sorted portion of the array. It is useful for sorting small arrays or when memory is limited, but performs more slowly as the number of elements increases compared to other sorting algorithms.
The selection sort algorithm works by iterating through an array, finding the minimum/maximum value, and swapping it into the correct sorted position. It does this by keeping track of the index of the minimum/maximum value found on each pass. The number of passes is equal to the length of the array. In each pass, it finds the minimum/maximum value and swaps it into the current place, sorting the array further.
The document discusses the convex hull algorithm. It begins by defining a convex hull as the shape a rubber band would take if stretched around pins on a board. It then provides explanations of extreme points, edges, and applications of convex hulls. Various algorithms for finding convex hulls are presented, including divide and conquer in O(n log n) time and Jarvis march in O(n^2) time in the worst case.
This document provides information about Python lists. Some key points:
- Lists can store multiple elements of any data type. They are versatile for working with multiple elements.
- Lists maintain element order and allow duplicate elements. Elements are accessed via indexes.
- Lists support operations like concatenation, membership testing, slicing, and methods to add/remove elements.
- Nested lists allow lists within lists, for representing matrices and other complex data structures.
This document discusses various sorting algorithms and their complexities. It begins by defining an algorithm and complexity measures like time and space complexity. It then defines sorting and common sorting algorithms like bubble sort, selection sort, insertion sort, quicksort, and mergesort. For each algorithm, it provides a high-level overview of the approach and time complexity. It also covers sorting algorithm concepts like stable and unstable sorting. The document concludes by discussing future directions for sorting algorithms and their applications.
The document discusses queue data structures. A queue is a linear data structure where additions are made at the end/tail and removals are made from the front/head, following a First-In First-Out (FIFO) approach. Common queue operations include add, remove, check if empty, check if full. A queue can be stored using either a static array or dynamic linked nodes. The key aspects are maintaining references to the head and tail of the queue.
NumPy is a Python library used for working with multidimensional arrays and matrices for scientific computing. It allows fast operations on arrays through optimized C code and is the foundation of the Python scientific computing stack. NumPy arrays can be created in many ways and support operations like indexing, slicing, broadcasting, and universal functions. NumPy provides many useful features for linear algebra, Fourier transforms, random number generation and more.
The document discusses the dynamic programming approach to solving the Fibonacci numbers problem and the rod cutting problem. It explains that dynamic programming formulations first express the problem recursively but then optimize it by storing results of subproblems to avoid recomputing them. This is done either through a top-down recursive approach with memoization or a bottom-up approach by filling a table with solutions to subproblems of increasing size. The document also introduces the matrix chain multiplication problem and how it can be optimized through dynamic programming by considering overlapping subproblems.
The document discusses insertion operations in arrays. It explains that to insert a new element into a one dimensional array, space must be created for the new element by moving existing elements. For example, to insert between the first and second element, the last N-1 elements would need to be moved down. An algorithm for insertion is provided that involves shifting elements after the insertion point over by one index to make space and then inserting the new element at that index.
This document discusses data types in C programming. It describes primitive data types like integers, floats, characters and their syntax. It also covers non-primitive data types like arrays, structures, unions, and linked lists. Arrays store a collection of similar data types, structures group different data types, and unions store different types in the same memory location. Linked lists are dynamic data structures using pointers. The document also provides overviews of stacks and queues, describing their LIFO and FIFO properties respectively.
---TABLE OF CONTENT---
Introduction
Differences between crisp sets & Fuzzy sets
Operations on Fuzzy Sets
Properties
MF formulation and parameterization
Fuzzy rules and Fuzzy reasoning
Fuzzy interface systems
Introduction to genetic algorithm
Analysis of Algorithm (Bubblesort and Quicksort)Flynce Miguel
Quicksort is a recursive divide-and-conquer algorithm that works by selecting a pivot element and partitioning the array into two subarrays of elements less than and greater than the pivot. It recursively sorts the subarrays. The divide step does all the work by partitioning, while the combine step does nothing. It has average case performance of O(n log n) but worst case of O(n^2). Bubble sort repeatedly swaps adjacent elements that are out of order until the array is fully sorted. It has a simple implementation but poor performance of O(n^2).
Design & Analysis of Algorithms Lecture NotesFellowBuddy.com
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
The document discusses several sorting algorithms: selection sort, bubble sort, quicksort, and merge sort. Selection sort has linear time complexity for swaps but quadratic time for comparisons. Bubble sort is quadratic time for both swaps and comparisons, making it the least efficient. Quicksort and merge sort are the fastest algorithms, both with logarithmic time complexity of O(n log n) for swaps and comparisons. Quicksort risks quadratic behavior in the worst case if pivots are chosen poorly, while merge sort requires more data copying between temporary and full lists.
Searching and sorting
Types of Searching
1. Linear Searching
2. Binary Searching
Types of Sorting
1.Selection Sort
2. Insertion Sort
3.Bubble Sort
And the examples of Linear searching, Binary Searching
And also the examples of Selection sort, Insertion sort and Bubble sort and describing them in detail in this ppt
This document discusses trees as a non-linear data structure. It defines key tree terminology such as root node, leaf node, and describes different types of trees including binary trees, binary search trees, and expression trees. Binary trees can have at most two children per node and are used in applications like expression evaluation. Binary search trees store elements in sorted order with left subtrees containing smaller elements and right subtrees containing larger elements. Expression trees represent arithmetic expressions with operators as internal nodes and operands as leaf nodes.
This document provides an overview of set theory, including definitions and concepts. It begins by defining a set as a collection of distinct objects, called elements or members. It describes how sets are denoted and provides examples. Key concepts covered include subsets, the empty set, set operations like union and intersection, and properties of sets. The document also discusses topics like the power set, Cartesian products, partitions, and the universal set. Overall, it serves as a comprehensive introduction to the basic ideas and terminology of set theory.
Selection sort is an in-place comparison sorting algorithm where the minimum element from the unsorted section of the list is selected in each pass and swapped with the first element. It has a time complexity of O(n2) making it inefficient for large lists. The algorithm involves dividing the list into sorted and unsorted sublists, finding the minimum element in the unsorted sublist, swapping it with the first element and moving the imaginary wall between the two sublists by one element. This process is repeated for n-1 passes to completely sort an input list of n elements. Pseudocode for the algorithm using a nested for loop to find the minimum element and swap it is also provided.
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
bubble sorting of an array in 8086 assembly languageBilal Amjad
The document describes bubble sort algorithm and includes code to implement it in assembly language. It lists group members and provides examples of bubble sort on sample data. It also includes pseudo code of the bubble sort procedure that sets the offset address and array size as inputs, sorts the array in ascending order, and returns the sorted array as output. The code segment implements bubble sort by getting input from the user, calling the bubble sort procedure to sort it, and displaying the sorted output.
The document discusses four sorting algorithms: selection sort, bubble sort, insertion sort, and merge sort. It provides pseudocode for the selection sort algorithm and describes how it works by making successive passes through an array and swapping elements until the array is fully sorted. Bubble sort and insertion sort are also described as making multiple passes through an array, comparing and swapping adjacent elements until the array is in order. Pseudocode is provided for the bubble sort and insertion sort algorithms.
The document discusses bubble sort, a simple sorting algorithm where each pair of adjacent elements is compared and swapped if in the wrong order. This process is repeated, with the highest/lowest value "bubbling" to the top/bottom, until the list is fully sorted. Although simple, bubble sort is slow compared to other algorithms. It can be useful if data is usually sorted but with some out-of-order elements. Pseudocode and an example are provided to illustrate the bubble sort process.
Sorting
NEED FOR SORTING
Insertion Sort
Illustration of Insertion Sort
Insertion Sort algorithm
code for Insertion Sort
advantages & disadvantages of Insertion Sort
best case and worst case of Insertion Sort
Selection sort
Illustration of Selection sort
Selection sort algorithm
code for Selection sort
worst case for selection Sort
Selection sort works by iteratively finding the minimum element in the unsorted portion of an array and swapping it into the sorted position. It has a time complexity of O(n^2) and is inefficient for large data sets. Bubble sort compares adjacent elements and swaps them if out of order, causing the largest elements to "bubble" to the end with each pass. Insertion sort inserts elements into the sorted portion of the array by shifting greater elements to make room. Both bubble and insertion sort have quadratic time complexity but insertion sort is generally more efficient due to less element swapping.
The document describes the bubble sort algorithm. It takes an array of numbers as input, such as {1,3,5,2,4,6}, and sorts it in ascending order through multiple passes where adjacent elements are compared and swapped if in the wrong order, resulting in the sorted array {1,2,3,4,5,6}. The algorithm works by making multiple passes through the array, swapping adjacent elements that are out of order on each pass until the array is fully sorted.
This document discusses bubble sort, selection sort, and insertion sort algorithms. It provides descriptions of how each algorithm works, including pseudocode examples. Bubble sort compares adjacent element pairs and swaps them if out of order, taking Ο(n2) time. Selection sort finds the minimum element and swaps it into the sorted portion of the array in each pass. Insertion sort maintains a sorted sub-list, inserting new elements in the appropriate place to build the full sorted list.
The document discusses various sorting algorithms and their complexity. It begins by defining sorting as arranging data in increasing or decreasing order. It then discusses the complexity of sorting algorithms in terms of comparisons, swaps, and assignments needed. Sorting algorithms are divided into internal sorts, which use only main memory, and external sorts, which use external storage like disks. Popular internal sorting algorithms discussed in detail include bubble sort, selection sort, insertion sort, and merge sort. Bubble sort has a time complexity of O(n2) while merge sort and quicksort have better time complexities of O(nlogn).
Bubble sort is a simple sorting algorithm that compares adjacent elements and swaps them if they are in the wrong order. This is repeated for each pair of adjacent elements with at least one swap happening per iteration until the list is fully sorted. Selection sort works by finding the minimum element in the unsorted section and swapping it with the leftmost element to build up the sorted section from left to right. Insertion sort maintains a sorted sub-list and inserts new elements into the correct position in the sub-list by shifting other elements over as needed.
This slide explains three (3) basic sorting algorithms with codes on github. Bubble sort, Selection sort and insertion sort.
visit https://github.com/EngrMikolo/BasicSortingAlgorithms to checkout the codes
This document proposes enhancements to selection sort and bubble sort algorithms. It describes an enhanced selection sort (ESS) that finds the maximum element and swaps it with the last element on each pass, reducing swap operations. An enhanced bubble sort (EBS) finds the minimum and maximum on each pass, swapping them with the first and last elements respectively. The algorithms are analyzed and applied to sort student records, showing ESS and EBS improve on standard selection and bubble sorts, and increase efficiency over shell sort variants.
MYSQL DATABASE MYSQL DATABASE MYSQL DATABASE BUBLESORT.pptxArjayBalberan1
The document describes the bubble sort algorithm. Bubble sort works by repeatedly comparing adjacent elements and swapping them if they are in the wrong order, causing the largest elements to "bubble" to the top of the list. This process is repeated, each time allowing the next largest element to bubble to the top, until the entire list is sorted after N-1 passes through the list. The algorithm is simple but inefficient for large lists, as it compares and swaps elements multiple times.
The document discusses sorting algorithms including bubble sort, selection sort, insertion sort, and merge sort. It provides pseudocode and explanations of how each algorithm works. Bubble sort, selection sort, and insertion sort have O(n2) runtime and are best for small datasets, while merge sort uses a divide-and-conquer approach to sort arrays with O(n log n) runtime, making it more efficient for large datasets. Radix sort is also discussed as an alternative sorting method that is optimized for certain data types.
Selection sort works by repeatedly finding the minimum element from the unsorted section of an array and placing it at the beginning. It maintains two subarrays - the sorted section and the unsorted section. In each iteration, the minimum element from the unsorted section is selected and swapped with the element in the sorted section. The algorithm has a runtime of Θ(n2) and is an in-place sorting algorithm. An example is provided to illustrate the steps of selection sort on a sample array.
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.
This document discusses selection sort, a sorting algorithm that arranges elements of an array from smallest to largest. It works by iterating through the array, selecting the minimum element, and swapping it into the correct position at the front of the unsorted section. The key steps are to select the minimum element, swap it with the first element, and repeat this process for the remainder of the array. Pseudocode is provided demonstrating the algorithm through a nested for loop structure.
This document provides an overview of sorting algorithms including insertion sort and bubble sort. It defines sorting as arranging data in a logical order and provides examples. Insertion sort works by scanning an array from the first element to the last, inserting each element into its proper place in the previously sorted subarray. Bubble sort compares adjacent elements and swaps them if out of order, pushing larger elements towards the end over multiple passes through the array. Pseudocode and an example are provided to illustrate the bubble sort algorithm.
The document describes several sorting algorithms:
1) Bubble sort repeatedly compares and swaps adjacent elements, moving the largest values to the end over multiple passes. It has a complexity of O(n^2).
2) Insertion sort inserts elements one by one into the sorted portion of the array by shifting elements and comparing. It is O(n^2) in worst case but O(n) if nearly sorted.
3) Selection sort finds the minimum element and swaps it into the first position in each pass to build the sorted array. It has complexity O(n^2).
4) Merge sort divides the array into halves recursively, then merges the sorted halves to produce the fully sorted array.
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.
Async-ronizing Success at Wix - Patterns for Seamless Microservices - Devoxx ...Natan Silnitsky
In a world where speed, resilience, and fault tolerance define success, Wix leverages Kafka to power asynchronous programming across 4,000 microservices. This talk explores four key patterns that boost developer velocity while solving common challenges with scalable, efficient, and reliable solutions:
1. Integration Events: Shift from synchronous calls to pre-fetching to reduce query latency and improve user experience.
2. Task Queue: Offload non-critical tasks like notifications to streamline request flows.
3. Task Scheduler: Enable precise, fault-tolerant delayed or recurring workflows with robust scheduling.
4. Iterator for Long-running Jobs: Process extensive workloads via chunked execution, optimizing scalability and resilience.
For each pattern, we’ll discuss benefits, challenges, and how we mitigate drawbacks to create practical solutions
This session offers actionable insights for developers and architects tackling distributed systems, helping refine microservices and adopting Kafka-driven async excellence.
Meet You in the Middle: 1000x Performance for Parquet Queries on PB-Scale Dat...Alluxio, Inc.
Alluxio Webinar
June 10, 2025
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
David Zhu (Engineering Manager @ Alluxio)
Storing data as Parquet files on cloud object storage, such as AWS S3, has become prevalent not only for large-scale data lakes but also as lightweight feature stores for training and inference, or as document stores for Retrieval-Augmented Generation (RAG). However, querying petabyte-to-exabyte-scale data lakes directly from S3 remains notoriously slow, with latencies typically ranging from hundreds of milliseconds to several seconds.
In this webinar, David Zhu, Software Engineering Manager at Alluxio, will present the results of a joint collaboration between Alluxio and a leading SaaS and data infrastructure enterprise that explored leveraging Alluxio as a high-performance caching and acceleration layer atop AWS S3 for ultra-fast querying of Parquet files at PB scale.
David will share:
- How Alluxio delivers sub-millisecond Time-to-First-Byte (TTFB) for Parquet queries, comparable to S3 Express One Zone, without requiring specialized hardware, data format changes, or data migration from your existing data lake.
- The architecture that enables Alluxio’s throughput to scale linearly with cluster size, achieving one million queries per second on a modest 50-node deployment, surpassing S3 Express single-account throughput by 50x without latency degradation.
- Specifics on how Alluxio offloads partial Parquet read operations and reduces overhead, enabling direct, ultra-low-latency point queries in hundreds of microseconds and achieving a 1,000x performance gain over traditional S3 querying methods.
Insurance policy management software transforms complex, manual insurance operations into streamlined, efficient digital workflows, enhancing productivity, accuracy, customer service, and profitability for insurers. Visit https://www.damcogroup.com/insurance/policy-management-software for more details!
Plooma is a writing platform to plan, write, and shape books your wayPlooma
Plooma is your all in one writing companion, designed to support authors at every twist and turn of the book creation journey. Whether you're sketching out your story's blueprint, breathing life into characters, or crafting chapters, Plooma provides a seamless space to organize all your ideas and materials without the overwhelm. Its intuitive interface makes building rich narratives and immersive worlds feel effortless.
Packed with powerful story and character organization tools, Plooma lets you track character development and manage world building details with ease. When it’s time to write, the distraction-free mode offers a clean, minimal environment to help you dive deep and write consistently. Plus, built-in editing tools catch grammar slips and style quirks in real-time, polishing your story so you don’t have to juggle multiple apps.
What really sets Plooma apart is its smart AI assistant - analyzing chapters for continuity, helping you generate character portraits, and flagging inconsistencies to keep your story tight and cohesive. This clever support saves you time and builds confidence, especially during those complex, detail packed projects.
Getting started is simple: outline your story’s structure and key characters with Plooma’s user-friendly planning tools, then write your chapters in the focused editor, using analytics to shape your words. Throughout your journey, Plooma’s AI offers helpful feedback and suggestions, guiding you toward a polished, well-crafted book ready to share with the world.
With Plooma by your side, you get a powerful toolkit that simplifies the creative process, boosts your productivity, and elevates your writing - making the path from idea to finished book smoother, more fun, and totally doable.
Get Started here: https://www.plooma.ink/
Advanced Token Development - Decentralized Innovationarohisinghas720
The world of blockchain is evolving at a fast pace, and at the heart of this transformation lies advanced token development. No longer limited to simple digital assets, today’s tokens are programmable, dynamic, and play a crucial role in driving decentralized applications across finance, governance, gaming, and beyond.
MOVIE RECOMMENDATION SYSTEM, UDUMULA GOPI REDDY, Y24MC13085.pptxMaharshi Mallela
Movie recommendation system is a software application or algorithm designed to suggest movies to users based on their preferences, viewing history, or other relevant factors. The primary goal of such a system is to enhance user experience by providing personalized and relevant movie suggestions.
Agentic Techniques in Retrieval-Augmented Generation with Azure AI SearchMaxim Salnikov
Discover how Agentic Retrieval in Azure AI Search takes Retrieval-Augmented Generation (RAG) to the next level by intelligently breaking down complex queries, leveraging full conversation history, and executing parallel searches through a new LLM-powered query planner. This session introduces a cutting-edge approach that delivers significantly more accurate, relevant, and grounded answers—unlocking new capabilities for building smarter, more responsive generative AI applications.
Traditional Retrieval-Augmented Generation (RAG) pipelines work well for simple queries—but when users ask complex, multi-part questions or refer to previous conversation history, they often fall short. That’s where Agentic Retrieval comes in: a game-changing advancement in Azure AI Search that brings LLM-powered reasoning directly into the retrieval layer.
This session unveils how agentic techniques elevate your RAG-based applications by introducing intelligent query planning, subquery decomposition, parallel execution, and result merging—all orchestrated by a new Knowledge Agent. You’ll learn how this approach significantly boosts relevance, groundedness, and answer quality, especially for sophisticated enterprise use cases.
Key takeaways:
- Understand the evolution from keyword and vector search to agentic query orchestration
- See how full conversation context improves retrieval accuracy
- Explore measurable improvements in answer relevance and completeness (up to 40% gains!)
- Get hands-on guidance on integrating Agentic Retrieval with Azure AI Foundry and SDKs
- Discover how to build scalable, AI-first applications powered by this new paradigm
Whether you're building intelligent copilots, enterprise Q&A bots, or AI-driven search solutions, this session will equip you with the tools and patterns to push beyond traditional RAG.
Artificial Intelligence Applications Across IndustriesSandeepKS52
Artificial Intelligence is a rapidly growing field that influences many aspects of modern life, including transportation, healthcare, and finance. Understanding the basics of AI provides insight into how machines can learn and make decisions, which is essential for grasping its applications in various industries. In the automotive sector, AI enhances vehicle safety and efficiency through advanced technologies like self-driving systems and predictive maintenance. Similarly, in healthcare, AI plays a crucial role in diagnosing diseases and personalizing treatment plans, while in financial services, it helps in fraud detection and risk management. By exploring these themes, a clearer picture of AI's transformative impact on society emerges, highlighting both its potential benefits and challenges.
AI-Powered Compliance Solutions for Global Regulations | Certivocertivoai
Certivo offers AI-powered compliance solutions designed to help businesses in the USA, EU, and UK simplify complex regulatory demands. From environmental and product compliance to safety, quality, and sustainability, our platform automates supplier documentation, manages certifications, and integrates with ERP/PLM systems. Ensure seamless RoHS, REACH, PFAS, and Prop 65 compliance through predictive insights and multilingual support. Turn compliance into a competitive edge with Certivo’s intelligent, scalable, and audit-ready platform.
Women in Tech: Marketo Engage User Group - June 2025 - AJO with AWSBradBedford3
Creating meaningful, real-time engagement across channels is essential to building lasting business relationships. Discover how AWS, in collaboration with Deloitte, set up one of Adobe's first instances of Journey Optimizer B2B Edition to revolutionize customer journeys for B2B audiences.
This session will share the use cases the AWS team has the implemented leveraging Adobe's Journey Optimizer B2B alongside Marketo Engage and Real-Time CDP B2B to deliver unified, personalized experiences and drive impactful engagement.
They will discuss how they are positioning AJO B2B in their marketing strategy and how AWS is imagining AJO B2B and Marketo will continue to work together in the future.
Whether you’re looking to enhance customer journeys or scale your B2B marketing efforts, you’ll leave with a clear view of what can be achieved to help transform your own approach.
Speakers:
Britney Young Senior Technical Product Manager, AWS
Erine de Leeuw Technical Product Manager, AWS
What is data visualization and how data visualization tool can help.pdfVarsha Nayak
An open source data visualization tool enhances this process by providing flexible, cost-effective solutions that allow users to customize and scale their visualizations according to their needs. These tools enable organizations to make data-driven decisions with complete freedom from proprietary software limitations. Whether you're a data scientist, marketer, or business leader, understanding how to utilize an open source data visualization tool can significantly improve your ability to communicate insights effectively.
Zoneranker’s Digital marketing solutionsreenashriee
Zoneranker offers expert digital marketing services tailored for businesses in Theni. From SEO and PPC to social media and content marketing, we help you grow online. Partner with us to boost visibility, leads, and sales.
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
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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
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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