Conversation with Merlin [email protected] · Mon Nov 13 2023

puedes explicar de forma detallada, sencilla, clara y directa la recursividad en C# con un codigo fuente de ejemplo con nodos en una lista enlazada

Claro, la recursividad en C# un concepto importante y útil. En el contexto de una lista enlazada, la recursividad se puede utilizar para realizar operaciones en los nodos de la lista enlazada de forma repetitiva Por ejemplo, podemos utilizar la recursividad para recorrer la lista, buscar un elemento específico, invertir la lista, entre otras operaciones.

Aquí te muestro un ejemplo de código en C# que ilustra la recursividad en una lista enlazada:

using System; public class Node { public int Data; public Node Next; public Node(int data) { Data = data; Next = null; } } public class LinkedList { private Node head; public void AddNode(int data) { Node newNode = new Node(data); if (head == null) { head = newNode; } else { Node current = head; while (current.Next != null) { current = current.Next; } current.Next = newNode; } } // Recorrido recursivo de la lista enlazada public void RecursiveTraversal(Node node) { if (node != null) { Console.WriteLine(node.Data); RecursiveTraversal(node.Next); } } class Program { static void Main() { LinkedList list = new LinkedList(); list.AddNode(5); list.AddNode(10); list.AddNode(15); Node startNode = list.head; list.RecTraversal(startNode); } }

En este ejemplo, la clase Node representa un nodo en la lista enlazada. La clase LinkedList tiene métodos para agregar nodos a la lista y para realizar el recorrido de la lista de forma recursiva.

La función RecursiveTraversal es un método que recorre la lista de forma recursiva. Comienza desde el nodo dado y muestra el valor del nodo actual, luego llama a sí misma con el siguiente nodo hasta que el nodo sea nulo, deteniendo la recursión.

Espero que este código te ayude a comprender cómo se puede implementar la recursividad en una lista enlazada en C#.

puedes explicar mas detalladamente como funciona la recursividad y cual la salida de la funcion RecursiveTraversal

Claro, la recursividad es un concepto en programación en el que una función se llama a sí misma para resolver un problema más pequeño. En el contexto de la función RecursiveTraversal que mencionaste antes, esta función se encarga de recorrer una lista enlazada de forma recursiva.

Cuando se llama a la función RecursiveTraversal con un nodo inicial, la función realiza las siguientes acciones:

  1. Verifica si el nodo dado no es nulo.
  2. Imprime el valor de datos del nodo actual.
  3. Llama a sí misma con el siguiente nodo en la lista.

Este proceso se repite hasta que se alcanza un nodo nulo, momento en el que la recursión se detiene.

Por ejemplo, si tenemos una lista enlazada con nodos que contienen los valores 5, 10, y 15, la salida de la función RecursiveTraversal sería:

5 10 15

Cada valor se imprimiría en una nueva línea, ya que la función RecursiveTraversal llama a sí misma para recorrer los nodos sucesivos hasta llegar al final de la lista.

en lugar de numeros digamos se almacenan nombres y edades solicitados desde teclado uno a uno desde un menu de opciones y luego hay dos opciones una para ordenar los nodos por edad y otra para imprimir los valores ingresados que deben ser recursivas

Entendido. Para implementar un programa en C# que logre las funciones que has mencionado, tendríamos que crear una implementación de una lista enlazada que permita la inserción de nombres y edades, además de dos funciones recursivas: una función para ordenar los nodos por edad y otra función para imprimir los valores ingresados. A continuación, te muestro un ejemplo de cómo se podría realizar esto:

using System; public class Node { public string Name; public int Age; public Node Next; public Node(string name, int age) { Name = name; Age = age; Next = null; } } public class LinkedList { private Node head; public void AddNode(string name, int age) { Node newNode = new Node(name, age); if (head == null) { head = newNode; } else { Node current = head; while (current.Next != null) { current = current.Next; } current.Next = newNode; } } public void RecursivePrint(Node node) { if (node != null) { Console.WriteLine($"Name: {node.Name}, Age: {node.Age}"); RecursivePrint(node.Next); } } public void RecursiveSortByAge() { head = MergeSort(head); } private Node MergeSort(Node start) { if (start == null || start.Next == null) { start; } Node middle = GetMiddle(start); Node nextToMiddle = middle.Next; middle.Next = null; Node left = MergeSort(start); Node right = Merge(nextToMiddle); return Merge(left, right); } private Node Merge(Node left, Node right) { if (left == null) { return right; } if (right == null) { return left; } Node result; if (left.Age <= right.Age) { result = left; result.Next = Merge(left.Next, right); } else { result = right; result.Next = Merge(left, right.Next); } return result; } } class Program { static void Main() { LinkedList list = new LinkedList(); // Aquí podrías implementar un menú que permita al usuario agregar nombres y edades a la lista // Por ejemplo: list.AddNode("Alice", 25); list.AddNode("Bob", 30); listNode("Charlie", 20); list.AddNode("David", 35); // Ordenar la lista edad list.RecursiveSortByAge(); // Imprimir los valores ingresados Node startNode = list.head; list.RecursivePrint(startNode); } }

En este ejemplo, se ha implementado una lista enlazada que almacena nombres y edades. La función RecursivePrint imprime los valores ingresados de forma recursiva, y la función RecursiveSortByAge ordena los nodos por edad utilizando el algoritmo Merge Sort de forma recursiva.

Espero que este código te ayude a comprender cómo implementar funciones recursivas en una lista enlazada en C# para ordenar y mostrar los valores ingresados.

"Dynamic list" redirects here. For the Wikipedia guideline which describes list articles which may never be completed, see Wikipedia:WikiProject Lists. A linked list is a sequence of nodes that contain two fields: data (an integer value here as an example) and a link to the next node. The last node is linked to a terminator used to signify the end of the list. In computer science, a linked list is a linear collection of data elements whose order is not given by their physical placement in memory. Instead, each element points to the next. It is a data structure consisting of a collection of nodes which together represent a sequence. In its most basic form, each node contains data, and a reference (in other words, a link) to the next node in the sequence. This structure allows for efficient insertion or removal of elements from any position in the sequence during iteration. More complex variants add additional links, allowing more efficient insertion or removal of nodes at arbitrary positions. A drawback of linked lists is that data access time is a linear function of the number of nodes for each linked list (I.e., the access time linearly increases as nodes are added to a linked list.) because nodes are serially linked so a node needs to be accessed first to access the next node (so difficult to pipeline). Faster access, such as random access, is not feasible. Arrays have better cache locality compared to linked lists. Linked lists are among the simplest and most common data structures. They can be used to implement several other common abstract data types, including lists, stacks, queues, associative arrays, and S-expressions, though it is not uncommon to implement those data structures directly without using a linked list as the basis. The principal benefit of a linked list over a conventional array is that the list elements can be easily inserted or removed without reallocation or reorganization of the entire structure because the data items do not need to be stored contiguously in memory or on disk, while restructuring an array at run-time is a much more expensive operation. Linked lists allow insertion and removal of nodes at any point in the list, and allow doing so with a constant number of operations by keeping the link previous to the link being added or removed in memory during list traversal. On the other hand, since simple linked lists by themselves do not allow random access to the data or any form of efficient indexing, many basic operationssuch as obtaining the last node of the list, finding a node that contains a given datum, or locating the place where a new node should be insertedmay require iterating through most or all of the list elements. History[edit] Linked lists were developed in 19551956, by Allen Newell, Cliff Shaw and Herbert A. Simon at RAND Corporation and Carnegie Mellon University as the primary data structure for their Information Processing Language (IPL). IPL was used by the authors to develop several early artificial intelligence programs, including the Logic Theory Machine, the General Problem Solver, and a computer chess program. Reports on their work appeared in IRE Transactions on Information Theory in 1956, and several conference proceedings from 1957 to 1959, including Proceedings of the Western Joint Computer Conference in 1957 and 1958, and Information Processing (Proceedings of the first UNESCO International Conference on Information Processing) in 1959. The now-classic diagram consisting of blocks representing list nodes with arrows pointing to successive list nodes appears in "Programming the Logic Theory Machine" by Newell and Shaw in Proc. WJCC, February 1957. Newell and Simon were recognized with the ACM Turing Award in 1975 for having "made basic contributions to artificial intelligence, the psychology of human cognition, and list processing". The problem of machine translation for natural language processing led Victor Yngve at Massachusetts Institute of Technology (MIT) to u

en.wikipedia.org

In this article, we will explore the problem of sorting a linked list using recursion. A linked list is a data structure composed of nodes where each node contains a data element and a reference (or pointer) to the next node in the sequence. The goal of this problem is to rearrange the elements in the linked list in ascending order. The problem can be defined as follows: Given a singly linked list, we need to sort its elements in ascending order using a recursive approach. The linked list can have any number of elements, and we want to modify it in-place, without creating a new linked list. Let's take an example to illustrate the problem. Consider a linked list with the following elements: 15 -> 5 -> 8 -> -5 -> 22 -> 3 -> 42 -> 16. Our goal is to sort this linked list in ascending order using recursion. // C Program // Sort linked list using recursion #include <stdio.h> #include <stdlib.h> //Define structure of custom linked list struct Node { int data; struct Node *next; }; //Insert a node in given linked list void insert(struct Node **head, int value) { //Create a node struct Node *node = (struct Node *) malloc(sizeof(struct Node)); if (node == NULL) { printf("Memory overflow\n"); } else { node->data = value; node->next = *head;*head = node; } } //This function are add new linked list element in sorted position void sorted_merge(struct Node *current, struct Node *element) { if (current->next == NULL) { //Add element at the end current->next = element; } else if (current->next->data >= element->data) { element->next = current->next; current->next = element; } else { sorted_merge(current->next, element); } } //Sorting the linked list elements using recursion void sort(struct Node **head) { //Base case if ( *head == NULL || ( *head)->next == NULL) { //When linked list is empty or only one element return; } //Get head element of linked list struct Node *element = ( *head); //Modify the head element of linked list ( *head) = element->next; //Separate previous head element element->next = NULL; //Recursively executing sort process sort(head); if (element->data <= ( *head)->data) { //Get a smallest Element element->next = ( *head);*head = element; } else { //Add element in sorted position sorted_merge( *head, element); } } //Display element of Node void display(struct Node *head) { while (head != NULL) { printf("%d ", head->data); head = head->next; } } int main() { struct Node *head = NULL; //Add element insert( & head, 16); insert( & head, 42); insert( & head, 3); insert( & head, 22); insert( & head, -5); insert( & head, 8); insert( & head, 5); insert( & head, 15); printf("\n Before Sort : "); display(head); sort( & head); printf("\n After Sort : "); display(head); }Output Before Sort : 15 5 8 -5 22 3 42 16 After Sort : -5 3 5 8 15 16 22 42 // Java Program // Sort linked list using recursion // Linked List node class Node { //Define linked list node elements public int data; public Node next; public Node(int data, Node next_node) { //set values of linked list this.data = data; this.next = next_node; } } class MyLinkedList { public Node head; //Class constructors LinkedList MyLinkedList() { this.head = null; } //insert newly element public void insert(int data) { //Make a new node of linked list Node node = new Node(data, this.head); if (node != null) { //Make a new head this.head = node; } } //display all Linked List elements public void display() { if (head != null) { Node temp = head; //iterating Linked List node elements, from the first node to last node while (temp != null) { // Display node values System.out.print(" " + temp.data); // Visit to next node temp = temp.next; } System.out.print("\n"); } else { System.out.println("Empty Linked list"); } } //This function are add new linked list element in sorted position public void sorted_merge(Node current, Node element) { if (current.next == null) { //Add element at the end current.next = element; } else if (current.next.data >= element.data) { element.next = current.next; cur

kalkicode.com

Given a singly linked list containing n nodes. The problem is to sort the list using the recursive selection sort technique. The approach should be such that it involves swapping node links instead of swapping node data. Examples: Input : 10 -> 12 -> 8 -> 4 -> 6 Output : 4 -> 6 -> 8 -> 10 -> 12 In Selection Sort, we first find the minimum element, swap it with the beginning node and recur for the remaining list. Below is the recursive implementation of these steps for the linked list. recurSelectionSort(head) if head->next == NULL return head Initialize min = head Initialize beforeMin = NULL Initialize ptr = head while ptr->next != NULL if min->data > ptr->next->data min = ptr->next beforeMin = ptr ptr = ptr->next if min != head swapNodes(&head, head, min, beforeMin) head->next = recurSelectionSort(head->next) return head swapNodes(head_ref, currX, currY, prevY) head_ref = currY prevY->next = currX Initialize temp = currY->next currY->next = currX->next currX->next = temp The swapNodes(head_ref, currX, currY, prevY) is based on the approach discussed here but it has been modified accordingly for the implementation of this post. Implementation: C++ #include <bits/stdc++.h> using namespace std; struct Node { int data; struct Node* next; }; void swapNodes(struct Node** head_ref, struct Node* currX, struct Node* currY, struct Node* prevY) { *head_ref = currY; prevY->next = currX; struct Node* temp = currY->next; currY->next = currX->next; currX->next = temp; } struct Node* recurSelectionSort(struct Node* head) { if (head->next == NULL) return head; struct Node* min = head; struct Node* beforeMin = NULL; struct Node* ptr; for (ptr = head; ptr->next != NULL; ptr = ptr->next) { if (ptr->next->data < min->data) { min = ptr->next; beforeMin = ptr; } } if (min != head) swapNodes(&head, head, min, beforeMin); head->next = recurSelectionSort(head->next); return head; } void sort(struct Node** head_ref) { if ((*head_ref) == NULL) return; *head_ref = recurSelectionSort(*head_ref); } void push(struct Node** head_ref, int new_data) { struct Node* new_node = (struct Node*)malloc(sizeof(struct Node)); new_node->data = new_data; new_node->next = (*head_ref); (*head_ref) = new_node; } void printList(struct Node* head) { while (head != NULL) { cout << head->data << " "; head = head->next; } } int main() { struct Node* head = NULL; push(&head, 6); push(&head, 4); push(&head, 8); push(&head, 12); push(&head, 10); cout << "Linked list before sorting:n"; printList(head); sort(&head); cout << "\nLinked list after sorting:n"; printList(head); return 0; } Java class GFG { static class Node { int data; Node next; }; static Node swapNodes( Node head_ref, Node currX, Node currY, Node prevY) { head_ref = currY; prevY.next = currX; Node temp = currY.next; currY.next = currX.next; currX.next = temp; return head_ref; } static Node recurSelectionSort( Node head) { if (head.next == null) return head; Node min = head; Node beforeMin = null; Node ptr; for (ptr = head; ptr.next != null; ptr = ptr.next) { if (ptr.next.data < min.data) { min = ptr.next; beforeMin = ptr; } } if (min != head) head = swapNodes(head, head, min, beforeMin); head.next = recurSelectionSort(head.next); return head; } static Node sort( Node head_ref) { if ((head_ref) == null) return null; head_ref = recurSelectionSort(head_ref); return head_ref; } static Node push( Node head_ref, int new_data) { Node new_node = new Node(); new_node.data = new_data; new_node.next = (head_ref); (head_ref) = new_node; return head_ref; } static void printList( Node head) { while (head != null) { System.out.print( head.data + " "); head = head.next; } } public static void main(String args[]) { Node head = null; head = push(head, 6); head = push(head, 4); head = push(head, 8); head = push(head, 12); head = push(head, 10); System.out.println( "Linked list before sorting:"); printList(head); head = sort(head); System.out.print( "\nLinked list after sorting:"); printList(head); } } Python class Node: def __init__(self

geeksforgeeks.org

puedes explicar detalladamente que hace cada una de las lineas de estas funciones y porque tantan funciones para ordenar la lista

Claro, con gusto puedo proporcionar una explicación detallada de las funciones que se usan comúnmente en el algoritmo de ordenamiento merge sort:

private Node MergeSort(Node start) { if (start == null || start.Next == null) { return start; } Node middle = GetMiddle(start); Node nextToMiddle = middle.Next; middle.Next = null; Node left = MergeSort(start); Node right = MergeSort(nextToMiddle); return Merge(left, right); } private Node GetMiddle(Node start) { if (start == null) { return start; } Node slowPtr = start; Node fastPtr = start; while (fastPtr.Next != null && fastPtr.Next.Next != null) { slowPtr = slowPtr.Next; fastPtr = fastPtr.Next.Next; } return slowPtr; } private Node Merge(Node left, Node right) { if (left == null) { return right; } if (right == null) { return left; } Node result; if (left.Age <= right.Age) { result = left; result.Next = Merge(left.Next, right); } else { result = right; result.Next = Merge(left, right.Next); } return result; }

La función MergeSort es la función principal que implementa el algoritmo de ordenamiento merge sort de manera recursiva. Explicando línea por línea:

  1. Verifica si la lista está vacía o si solo contiene un elemento. En estos casos, devuelve la lista original, ya que una lista vacía o con un solo elemento ya se considera ordenada.

  2. Encuentra el nodo medio de la lista usando la función GetMiddle, dividiendo la lista en dos sub-listas.

  3. Realiza la llamada recursiva a MergeSort para ordenar las dos sub-listas por separado.

  4. Une las sublistas ordenadas utilizando la función Merge.

La función GetMiddle encuentra el nodo medio de una lista dada. Utiliza dos punteros, uno que se desplaza a una velocidad normal y otro que se desplaza a una velocidad doble. Cuando el puntero que avanza a doble velocidad llega al final de la lista, el puntero que avanza a velocidad normal estará en la mitad.

La función Merge combina dos sublistas ordenadas en una sola lista ordenada. Compara los nodos de ambas sublistas y reconstr una lista única con ellos ordenados de forma ascendente según la edad.

La razón de tener varias funciones para ordenar la lista es que el enfoque del algoritmo de ordenamiento merge sort es dividir la lista en sublistas más pequeñas, ordenarlas y luego combinarlas. Cada función desempeña un papel específico en este proceso, lo que hace que el algoritmo sea más claro y modular en su implementación. Este enfoque facilita el mantenimiento y la comprensión del código, lo que lo hace más robusto y flexible.

Merge sortAn example of merge sort. First, divide the list into the smallest unit (1 element), then compare each element with the adjacent list to sort and merge the two adjacent lists. Finally, all the elements are sorted and merged.ClassSorting algorithmData structureArrayWorst-case performanceBest-case performance typical, natural variantAverage performanceWorst-case space complexity total with auxiliary, auxiliary with linked lists[1] In computer science, merge sort (also commonly spelled as mergesort) is an efficient, general-purpose, and comparison-based sorting algorithm. Most implementations produce a stable sort, which means that the relative order of equal elements is the same in the input and output. Merge sort is a divide-and-conquer algorithm that was invented by John von Neumann in 1945.[2] A detailed description and analysis of bottom-up merge sort appeared in a report by Goldstine and von Neumann as early as 1948.[3] Algorithm[edit] Conceptually, a merge sort works as follows: Divide the unsorted list into n sublists, each containing one element (a list of one element is considered sorted). Repeatedly merge sublists to produce new sorted sublists until there is only one sublist remaining. This will be the sorted list. Top-down implementation[edit] Example C-like code using indices for top-down merge sort algorithm that recursively splits the list (called runs in this example) into sublists until sublist size is 1, then merges those sublists to produce a sorted list. The copy back step is avoided with alternating the direction of the merge with each level of recursion (except for an initial one-time copy, that can be avoided too). To help understand this, consider an array with two elements. The elements are copied to B[], then merged back to A[]. If there are four elements, when the bottom of the recursion level is reached, single element runs from A[] are merged to B[], and then at the next higher level of recursion, those two-element runs are merged to A[]. This pattern continues with each level of recursion. // Array A[] has the items to sort; array B[] is a work array. void TopDownMergeSort(A[], B[], n) { CopyArray(A, 0, n, B); // one time copy of A[] to B[] TopDownSplitMerge(A, 0, n, B); // sort data from B[] into A[] } // Split A[] into 2 runs, sort both runs into B[], merge both runs from B[] to A[] // iBegin is inclusive; iEnd is exclusive (A[iEnd] is not in the set). void TopDownSplitMerge(B[], iBegin, iEnd, A[]) { if (iEnd - iBegin <= 1) // if run size == 1 return; // consider it sorted // split the run longer than 1 item into halves iMiddle = (iEnd + iBegin) / 2; // iMiddle = mid point // recursively sort both runs from array A[] into B[] TopDownSplitMerge(A, iBegin, iMiddle, B); // sort the left run TopDownSplitMerge(A, iMiddle, iEnd, B); // sort the right run // merge the resulting runs from array B[] into A[] TopDownMerge(B, iBegin, iMiddle, iEnd, A); } // Left source half is A[ iBegin:iMiddle-1]. // Right source half is A[iMiddle:iEnd-1 ]. // Result is B[ iBegin:iEnd-1 ]. void TopDownMerge(B[], iBegin, iMiddle, iEnd, A[]) { i = iBegin, j = iMiddle; // While there are elements in the left or right runs... for (k = iBegin; k < iEnd; k++) { // If left run head exists and is <= existing right run head. if (i < iMiddle && (j >= iEnd || A[i] <= A[j])) { B[k] = A[i]; i = i + 1; } else { B[k] = A[j]; j = j + 1; } } } void CopyArray(A[], iBegin, iEnd, B[]) { for (k = iBegin; k < iEnd; k++) B[k] = A[k]; } Sorting the entire array is accomplished by TopDownMergeSort(A, B, length(A)). Bottom-up implementation[edit] Example C-like code using indices for bottom-up merge sort algorithm which treats the list as an array of n sublists (called runs in this example) of size 1, and iteratively merges sub-lists back and forth between two buffers: // array A[] has the items to sort; array B[] is a work array void BottomUpMergeSort(A[], B[], n) { // Each 1-element run in A is already "sorted". // Make successively lon

en.wikipedia.org

The Merge Sort uses a recursive algorithm to achieve its results. The divide-and-conquer algorithm breaks down a big problem into smaller, more manageable pieces that look similar to the initial problem. It then solves these subproblems recursively and puts their solutions together to solve the original problem. By the end of this tutorial, you will have a better understanding of the technical fundamentals of the Merge Sort with all the necessary details, along with practical examples. What Is a Merge Sort Algorithm? Merge sort is one of the most efficient sorting algorithms. It is based on the divide-and-conquer strategy. Merge sort continuously cuts down a list into multiple sublists until each has only one item, then merges those sublists into a sorted list. What Is a Divide and Conquer Algorithm? Divide-and-conquer recursively solves subproblems; each subproblem must be smaller than the original problem, and each must have a base case. A divide-and-conquer algorithm has three parts: Divide up the problem into a lot of smaller pieces of the same problem. Conquer the subproblems by recursively solving them. Solve the subproblems as base cases if they're small enough. To find the solutions to the original problem, combine the solutions of the subproblems. How Does the Merge Sort Algorithm Work? Merge sort algorithm can be executed in two ways: Top-down Approach It starts at the top and works its way down, splitting the array in half, making a recursive call, and merging the results until it reaches the bottom of the array tree. Bottom-Up Approach The iterative technique is used in the Bottom-Up merge sort approach. It starts with a "single-element" array and then merges two nearby items while also sorting them. The combined-sorted arrays are merged and sorted again until only one single unit of the sorted array remains. You have now explored two approaches to execute merge sort. Now, you will learn to implement one of the approaches in a code editor. How to Implement the Merge Sort Algorithm? It splits the input array in half, calls itself for each half, and then combines the two sorted parts. Merging two halves is done with the merge() method. Merge (array[], left, mid, right) is a crucial process that assumes array[left..mid] and array[mid+1..right] are both sorted sub-arrays and merges them into one. Code: // A C++ program to Sort an array using merge sort algorithm #include <bits/stdc++.h> using namespace std; // Merges two subarrays of array[]. // First subarray is arr[begin..mid] // Second subarray is arr[mid+1..end] void merge(int array[], int const left, int const mid, int const right) { int const subarrayone = mid - left + 1; int const subarraytwo = right - mid; // Create temp arrays int *leftarray = new int[subarrayone], *rightarray = new int[subarraytwo]; // Copy data to temp arrays leftarray[] and rightarray[] for (int i = 0; i < subarrayone; i++) leftarray[i] = array[left + i]; for (int j = 0; j < subarraytwo; j++) rightarray[j] = array[mid + 1 + j]; int indexofsubarrayone = 0, // Initial index of first sub-array indexofsubarraytwo = 0; // Initial index of second sub-array int indexofmergedarray = left; // Initial index of merged array // Merge the temp arrays back into array[left..right] while (indexofsubarrayone < subarrayone && indexofsubarraytwo < subarraytwo) { if (leftarray[indexofsubarrayone] <= rightarray[indexofsubarraytwo]) { array[indexofmergedarray] = leftarray[indexofsubarrayone]; indexofsubarrayone++; } else { array[indexofmergedarray] = rightarray[indexofsubarraytwo]; indexofsubarraytwo++; } indexofmergedarray++; } // Copy the remaining elements of // left[], if there are any while (indexofsubarrayone < subarrayone) { array[indexofmergedarray] = leftarray[indexofsubarrayone]; indexofsubarrayone++; indexofmergedarray++; } // Copy the remaining elements of // right[], if there are any while (indexofsubarraytwo < subarraytwo) { array[indexofmergedarray] = rightarray[indexofsubarraytwo]; indexofsubarr

simplilearn.com

Merge sort is one of the most efficient sorting algorithms. It works on the principle of Divide and Conquer based on the idea of breaking down a list into several sub-lists until each sublist consists of a single element and merging those sublists in a manner that results into a sorted list. Merge Sort Working Rule The concept of Divide and Conquer involves three steps: Divide the problem into multiple subproblems. Solve the Sub Problems. The idea is to break down the problem into atomic subproblems, where they are actually solved. Combine the solutions of the subproblems to find the solution of the actual problem. So, the merge sort working rule involves the following steps: Divide the unsorted array into subarray, each containing a single element. Take adjacent pairs of two single-element array and merge them to form an array of 2 elements. Repeat the process till a single sorted array is obtained. An array of Size N is divided into two parts N/2 size of each. then those arrays are further divided till we reach a single element. The base case here is reaching one single element. When the base case is hit, we start merging the left part and the right part and we get a sorted array at the end. Merge sort repeatedly breaks down an array into several subarrays until each subarray consists of a single element and merging those subarrays in a manner that results in a sorted array. Merge Sort Algorithm Flow Array = {70,50,30,10,20,40,60} MergeSort We repeatedly break down the array in two parts, the left part, and the right part. the division takes place from the mid element. We divide until we reach a single element and then we start combining them to form a Sorted Array. Merge Sort Implementations We will implement the merge sort algorithm in Java, C, and Python. 1. Java Implementation package com.journaldev.ds; public class MergeSort { public static void main(String[] args) { int[] arr = { 70, 50, 30, 10, 20, 40, 60 }; int[] merged = mergeSort(arr, 0, arr.length - 1); for (int val : merged) { System.out.print(val + " "); } } public static int[] mergeTwoSortedArrays(int[] one, int[] two) { int[] sorted = new int[one.length + two.length]; int i = 0; int j = 0; int k = 0; while (i < one.length && j < two.length) { if (one[i] < two[j]) { sorted[k] = one[i]; k++; i++; } else { sorted[k] = two[j]; k++; j++; } } if (i == one.length) { while (j < two.length) { sorted[k] = two[j]; k++; j++; } } if (j == two.length) { while (i < one.length) { sorted[k] = one[i]; k++; i++; } } return sorted; } public static int[] mergeSort(int[] arr, int lo, int hi) { if (lo == hi) { int[] br = new int[1]; br[0] = arr[lo]; return br; } int mid = (lo + hi) / 2; int[] fh = mergeSort(arr, lo, mid); int[] sh = mergeSort(arr, mid + 1, hi); int[] merged = mergeTwoSortedArrays(fh, sh); return merged; } } OUTPUT Merge Sort Algorithm Java Implementation 2. C Implementation #include <stdio.h> void merge(int arr[], int l, int m, int r) { int i, j, k; int n1 = m - l + 1; int n2 = r - m; /* create temp arrays */ int L[n1], R[n2]; /* Copy data to temp arrays L[] and R[] */ for (i = 0; i < n1; i++) L[i] = arr[l + i]; for (j = 0; j < n2; j++) R[j] = arr[m + 1+ j]; /* Merge the temp arrays back into arr[l..r]*/ i = 0; // Initial index of first subarray j = 0; // Initial index of second subarray k = l; // Initial index of merged subarray while (i < n1 && j < n2) { if (L[i] <= R[j]) { arr[k] = L[i]; i++; } else { arr[k] = R[j]; j++; } k++; } /* Copy the remaining elements of L[], if there are any */ while (i < n1) { arr[k] = L[i]; i++; k++; } /* Copy the remaining elements of R[], if there are any */ while (j < n2) { arr[k] = R[j]; j++; k++; } } /* l is for left index and r is the right index of the sub-array of arr to be sorted */ void mergeSort(int arr[], int l, int r) { if (l < r) { // Same as (l+r)/2, but avoids overflow for // large l and h int m = l+(r-l)/2; // Sort first and second halves mergeSort(arr, l, m); mergeSort(arr, m+1, r); merge(arr, l, m, r); } } void prin

digitalocean.com

puedes explicar detalladamente que hace cada una de las lineas de estas funciones y porque tantas funciones para ordenar la lista public void RecursiveSortByAge() { head = MergeSort(head); }

private Node MergeSort(Node start) { if (start == null || start.Next == null) { return start; } Node middle = GetMiddle(start); Node nextToMiddle = middle.Next; middle.Next = null; Node left = MergeSort(start); Node right = MergeSort(nextToMiddle); return Merge(left, right); } private Node Merge(Node left, Node right) { if (left == null) { return right; } if (right == null) { return left; } Node result; if (left.Age <= right.Age) { result = left; result.Next = Merge(left.Next, right); } else { result = right; result.Next = Merge(left, right.Next); } return result; }

La función RecursiveSortByAge es el punto de entrada para iniciar el proceso de ordenamiento recursivo de la lista por edad. Esta función asigna el resultado del ordenamiento, devuelto por la función MergeSort, al nodo de inicio de la lista.

La función MergeSort realiza el ordenamiento propiamente dicho de forma recursiva utilizando el algoritmo merge sort. Ahora, detallaré cada línea de esta función:

  1. La función verifica si el nodo de inicio es nulo o si el siguiente nodo es nulo, lo que indicaría que la lista o la porción actual de la lista ya está ordenada. En tales casos, la función devuelve el nodo actual sin realizar más operaciones.

  2. Luego, la función encuentra el nodo medio de la lista mediante la función GetMiddle. Posteriormente, la lista se divide en dos partes, usando el nodo medio y el siguiente nodo al medio.

  3. Se realizan llamadas recursivas a MergeSort para ordenar las sublistas izquierda y derecha por separado.

  4. Finalmente, se invoca la función Merge para combinar y ordenar las sublistas izquierda y derecha de manera consecutiva.

La función GetMiddle se encarga de encontrar el nodo medio de la lista, lo que es esencial para dividir la lista en dos partes iguales.

La función Merge combina dos sublistas ordenadas en una sola lista ordenada. Comparando los nodos de ambas sublistas, la función va creando la lista ordenada con base en la edad.

Utilizar múltiples funciones para ordenar la lista es una característica fundamental del algoritmo merge sort. Las funciones están diseñadas de manera modular para dividir la tarea de ordenamiento en pasos más simples y manejables. Esta modularidad permite reutilizar el código y facilita la comprensión y el mantenimiento. El enfoque recursivo utilizado por el merge sort es ideal para manipular y ordenar estructuras de datos complejas, como las listas enlazadas, de manera eficiente.

Merge sort in C# is one of the algorithms that we can use to sort elements. Merge Sort is known to be efficient as it uses the divide and conquer strategy that we are going to discuss as we implement it. To download the source code for this article, you can visit our GitHub repository. Lets start. What is Merge Sort? Merge Sort achieves its purpose using a two-step process: Divide: merge sort starts by dividing the input array into two halves. The algorithm recursively calls itself for each of those halves until there are no half-arrays to divide during the sorting process. Conquer: the algorithm sorts and merges the sub-arraysin this step to return an array whose values are sorted.Support Code Maze on Patreon to get rid of ads and get the best discounts on our products! Generally, we use these high-level steps when sorting an array or a list with a merge sort: Step 1: Check if the array has one element. If it does, it means all the elements are sorted. Step 2: Use recursion to divide the array into two halves until we can't divide it anymore. Step 3: Merge the arrays into a new array whose values are sorted. How Does Merge Sort Algorithm Work? Lets say we want to sort an array that has eight elements: int[] array = { 73, 57, 49, 99, 133, 20, 1, 34 }; Dividing Arrays Using the merge sort algorithm, lets start subdividing the array into equal halves. We can see here that in the first iteration, the array is split into two equal arrays of half the size (4): 73, 57, 49, 99133, 20, 1, 34 Now, we divide these arrays into halves without changing the sequence of the elements in the original array: 73, 57 49, 99 133, 20 1, 34 We are going to subdivide the arrays further into arrays that have one element each (we achieve the atomic value in this step): 73 5749 99 133 20 1 34 After subdividing the arrays, we are going to now start merging them while comparing the elements and placing them in the right order. Merging and Sorting This algorithm requires an extra array to store the sorted elements. The algorithm starts by traversing the subarrays that hold 73 and 57. 57 is less than 73, so we swap their positions while merging them into a new array that has two elements: 57, 73 Next, we traverse the subarrays that hold 49 and 99 while comparing the elements at each position. Since 49 is less than 99, the merged array of size 2 becomes: 57, 73 49, 99 The same process applies here as the algorithm starts traversing the subarrays while comparing the values they hold in each position. 20 is less than 133, so the algorithm swaps their positions when merging them and the array becomes: 57, 73 49, 99 20, 133 1 and 34 are already in the correct order so we dont swap their positions when combining them back into an array: 57, 73 49, 99 20, 1331, 34 Next Merge Iteration In the next iteration of the merging process, the arrays should have four elements each, sorted in the correct order. The algorithm starts traversing the two subarrays of size two while comparing the values at each position. In the first position, 49 is less than 57. We move 49 to the first position followed by 57. Now we move on to 73 and since it is less than 99 they dont need to swap positions. So, the new merged array of size 4 is: 49, 57, 73, 99 We repeat the same process for the right half of the array. The algorithm compares the elements on each subarray while merging them into a new array. 1 is less than 20 so it becomes the first element in the merged array followed by 20. The algorithm compares 133 with 34 and adds 34 first into the new array before adding 133. So, the new array after the merging process is: 1, 20, 34, 133 In the next iteration of the merging process, the array should have eight sorted elements (same as the original array). The algorithm starts traversing the subarrays while comparing the values at each position while adding them to a new array that has sorted elements. For example, at position 1 in the subarrays, 1 is less than 49, so we store 1 as the fi

code-maze.com

Merge Sort in C# Merge Sort is a popular sorting algorithm that is widely used in computer science and software development. It is an efficient, stable, and a divide-and-conquer algorithm that is based on the concept of merging two sorted arrays into one. The algorithm is particularly useful when working with large datasets, as it has a time complexity of O(n log n), which is considered to be one of the most efficient sorting algorithms. In this article, we will discuss how to implement Merge Sort in C# and its advantages and disadvantages. The first step in implementing Merge Sort in C# is to create a method that will take in an array of integers as an argument. This method should then divide the array into two smaller arrays, sort them, and then merge them back together. The method should continue dividing the array until it reaches the base case, which is when the array is of size 1. Custom Merge sort example Here is an example of how to implement Merge Sort in C#: 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364 class Sort { public static void MergeSort(int[] arr) { if (arr.Length <= 1) return; int mid = arr.Length / 2; int[] left = new int[mid]; int[] right = new int[arr.Length - mid]; for (int i = 0; i < mid; i++) left[i] = arr[i]; for (int i = mid; i < arr.Length; i++) right[i - mid] = arr[i]; MergeSort(left); MergeSort(right); Merge(arr, left, right); } private static void Merge(int[] arr, int[] left, int[] right) { int i = 0, j = 0, k = 0; while (i < left.Length && j < right.Length) { if (left[i] <= right[j]) arr[k++] = left[i++]; else arr[k++] = right[j++]; } while (i < left.Length) arr[k++] = left[i++]; while (j < right.Length) arr[k++] = right[j++]; } static void Main() { int[] ints = { 8, 875, 7, 9, 764, 55 }; Console.WriteLine("Original array:"); foreach (int i in ints) { Console.WriteLine(i); } MergeSort(ints); Console.WriteLine("Sorted array:"); foreach (int i in ints) { Console.WriteLine(i); } } } In the above code, the MergeSort() method is the main method that is responsible for dividing the array, sorting the sub-arrays, and merging them back together. The method checks if the array is of size 1 and returns if it is. If not, the method divides the array into two smaller arrays and calls the MergeSort() method recursively on each of them. Once the sub-arrays are sorted, the method calls the Merge() method, which is responsible for merging the two sorted arrays back together. The Merge() method uses three pointers, i, j, and k, to iterate through the left and right arrays. The method compares the elements of the left and right arrays and assigns the smaller value to the original array. The method continues until all the elements in the left and right arrays have been added to the original array. Visualization You can imagine this sorting process as a tree of partition down to one element. That means every recursive call divides our array by 2. Take a look on the example below: Here you can see how our array was divided. But the most interesting part is how it will be merged back: On this schema, you can see that the array sort itself during each merge. And this is the magic part of this sort. When you know that each part of the merged arrays is sorted, it is easier to put them together in sorted order. You just have to go through them and check which value should go first and which after. For example, lets take the top of our recursive tree (1,3,7 and 0,2,9). We check the first values. 0 is the smallest so we push it as the first value to the result array ( Current state 1,3,7 and 2,9; Result array: 0 ); We do the same with 1 and 2 ( Current state 3,7 and 2,9; Result array: 0,1 ); We do the same with 2 and 3 ( Current state 3,7 and 9; Result array: 0,1,2 ) We do the same with 3 and 9 ( Current state 7 and 9; Result array: 0,1,2,3 ) We do the same with 7 and 9 ( Current state {} and 9; Result array: 0,1,2,3,7 ) And finally just pushing 9 (

tutorials.eu

Introduction Sorting is a common activity in the programming work. Merge-Sort is one of the best implementation for sorting, since its running on O(nlogn) - which is the best run-time available for sorting. On one of my projects I had a requirement to sort Objects. After short research, I decided to write a Generic Merge-Sort Class that will work for me. During the work, I found a few major points that should be considered while using that Generic Class for Sorting. Background Merge Sort (pseudo-code in the picture) is a recursive algorithm, that splits an array of the objects to 2 sub arrays - A,B (which initialize to infinite values), sorts this into 2 sub-arrays and finally merges them. (Hence, merge sort) Using the Code For using the Generic Merge Sort, you should (probably) know what is the objects parameter by which the objects will be sorted. Now, since the 2 sub-arrays should be initialized with an infinite value, you should use the default-ctor to make that initialization. Example: For sorting an array of Persons Objects that will be sorted according to their ID number (from low to high), you should declare infinite ID-number (infinite int32 value) in the default-ctor. (I know This is not an elegant implementation, but In that case there is no alternative.) class MergeSort { // Sorts an array of Objects // IComparable - use 'CompareTo' to compare objects // where T : new() - need to create a new Type 'T' object inside the method public static T[] Merge_Sort<T>(T[] X) where T : IComparable, new() { int n = X.Length; X = MegrgeSort_Internal(X, n); return X; } // Internal method for sorting private static T[] MegrgeSort_Internal<T>(T[] X, int n) where T : IComparable, new() { // Define 2 aid Sub-Arrays T[] A = new T[(n / 2) + 2]; T[] B = new T[(n / 2) + 2]; // Initialize the 2 Sub-Arrays with an infinite 'sorting parameter' // Therefor, You must include a default ctor in your class // which will initialize an infinite value - To the sorting parameter // using 'where T : new()' here for (int i = 0; i < A.Length; i++) { A[i] = new T(); ; B[i] = new T(); } // Recursive Stop-Condition, Sorting a Basic Array (Size 2) if (n == 2) { int CompareValue = X[0].CompareTo(X[1]); if (CompareValue > 0) { T tempT = X[0]; X[0] = X[1]; X[1] = tempT; } } else { // The Sub-Arrays Size is Large than 2 if (n > 2) { int m = n / 2; // Initialize the 2 Sub-Arrays (The first relevant values) for (int i = 0; i < m; i = i + 1) { A[i] = X[i]; } for (int j = m; j < n; j++) { B[j - m] = X[j]; } // 2 Recursive Calling, Sorting Sub-Arrays A = MegrgeSort_Internal(A, m); B = MegrgeSort_Internal(B, n - m); // Merging the Sorted Sub-Arrays into the main Array int p = 0; int q = 0; for (int k = 0; k < n; k++) { { int CompareValure = A[p].CompareTo(B[q]); if (CompareValure == 0 || CompareValure == -1) { X[k] = A[p]; p = p + 1; } else { X[k] = B[q]; q = q + 1; } } } } // if } // else return X; } // MegrgeSort_Internal } Points of Interest When writing a class for using Generic Merge Sort, you should implement the IComparable interface since the code using the CompareTo function - to compare objects. You should also write your own overloading operators that will be in use to compare objects: ==, !=, >, >=, <, <= plus Equals and GetHashCode. Please notice that GetHashCode will be in use by the CLR implicitly - so you must write your own implementation that will return the same value for the same object. There are two projects attached: The first one is a simple (int32) Implementation, that sorts an array of integers. The second one is the Generic Merge Sort, Plus a simple Person class that will demonstrate the using of the Generic code. License This article, along with any associated source code and files, is licensed under GPL (GPLv3)

nagalr.github.io