Introducción

1

¿Qué es un grafo?

2

¿Qué es un árbol?

3

¿Qué es recursión?

4

Aplicaciones reales de grafos y árboles

5

Formas de representar un grafo

DFS

6

Análisis de DFS: algoritmo de búsqueda en profundidad

7

Programando DFS de forma recursiva

8

Otras formas de programar DFS

9

Recorridos y profundidad de un Árbol

10

Sum Root to Leaf Numbers: análisis del problema

11

Solución de Sum Root to Leaf Numbers

12

Playground: Sum Root to Leaf Numbers

13

Programando Sum Root to Leaf Numbers en Golang

14

Number of Islands: análisis del problema

15

Solución de Number of Islands

16

Playground: Number of Islands

17

Programando Number of Islands en Python

18

Ejercicios recomendados de DFS

19

Ejercicios resueltos de DFS

BFS

20

Análisis de BFS: algoritmo de búsqueda en anchura

21

Programando BFS con Python

22

Minimum Knights Moves (movimientos de caballo en ajedrez): análisis del problema

23

Solución de Minimum Knights Moves

24

Playground: Minimum Knights Moves

25

Programando Minimum Knights Moves con Python

26

Rotting Oranges: análisis del problema

27

Solución de Rotting Oranges

28

Playground: Rotting Oranges

29

Rotting Oranges con Java

30

Shortest Bridge Between Islands: análisis del problema

31

Solución de Shortest Bridge Between Islands

32

Playground: Shortest Bridge Between Islands

33

Programando Shortest Bridge Between Islands con Python

34

Ejercicios recomendados de BFS

35

Ejercicios resueltos de BFS

Backtrack

36

Algoritmo de Backtrack

37

Letter Combinations of a Phone Number: análisis del problema

38

Solución de Letter Combinations of a Phone Number

39

Programando Letter Combinations of a Phone Number con C++

40

Playground: Letter Combinations of a Phone Number

41

Restore IP Addresses: análisis del problema

42

Programando Restore IP Addresses con C++

43

Playground: Restore IP Addresses

44

Word Search: análisis del problema

45

Solución de Word Search

46

Playgrund: Word Search

47

Programando Word Search JavaScript

48

Reto: N Queens Puzzle

49

Ejercicios recomendados de Backtrack

50

Ejercicios resueltos de Backtrack

Próximos pasos

51

¿Qué otros algoritmos y tipos de grafos puedes aprender?

52

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Análisis de BFS: algoritmo de búsqueda en anchura

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Resources

What is the BFS algorithm and how does it work?

Welcome to a new tour through the fascinating world of algorithms. Today, we will explore the Breadth-First Search (BFS) algorithm, a fundamental technique for traversing data structures such as graphs, arrays or trees. If you've ever wondered how to prioritize certain tasks or searches, you'll find that BFS is essential for organizing elements according to their hierarchy, addressing each level completely before moving on to the next.

How does BFS address hierarchy in trees or networks?

The BFS algorithm, unlike Depth-First Search (DFS), focuses on expanding horizontally rather than going deep. Imagine you are exploring a binary tree; BFS starts at the root and traverses all nodes at the next level before moving further down. This method is especially useful when we want to approach elements according to their hierarchy or importance.

In practical terms, when using BFS:

  • You traverse level by level, prioritizing the nodes that are closest to the root.
  • You will be able to visualize the tree hierarchically, identifying which elements are of higher priority or importance.
  • This way you avoid going unnecessarily deep, getting a complete review of the level before descending.

What is the time complexity of BFS?

The efficiency of the algorithm is a crucial factor in its implementation. BFS has a time complexity of O(V + E), where V represents the number of nodes (vertices) and E the number of edges (links) in the network. According to this metric:

  • It is equivalent to DFS, since both must evaluate the entire tree or network.
  • The choice between one or the other depends mostly on the nature of the data hierarchy to be explored or solved.

In which situations is it preferable to use BFS?

The selection of BFS as a search algorithm is particularly useful in several specific scenarios:

  • Shortest path searches in unweighted network: Ideal for finding efficient paths.
  • Connectivity analysis: To verify if all nodes in a network are connected.
  • Hierarchical level detection: Very useful in structures where the organization by levels has a significant value, e.g. hierarchical management systems.

At the end of the day, BFS is your ally when you need hierarchical clarity and efficiency in complex data structures. With its layered approach, it ensures you an orderly and structured view, vital for many real-world applications. keep exploring and expanding your knowledge! 🧠✨

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