Introducción
¿Qué es un grafo?
¿Qué es un árbol?
¿Qué es recursión?
Aplicaciones reales de grafos y árboles
Formas de representar un grafo
DFS
Análisis de DFS: algoritmo de búsqueda en profundidad
Programando DFS de forma recursiva
Otras formas de programar DFS
Recorridos y profundidad de un Árbol
Sum Root to Leaf Numbers: análisis del problema
Solución de Sum Root to Leaf Numbers
Playground: Sum Root to Leaf Numbers
Programando Sum Root to Leaf Numbers en Golang
Number of Islands: análisis del problema
Solución de Number of Islands
Playground: Number of Islands
Programando Number of Islands en Python
Ejercicios recomendados de DFS
Ejercicios resueltos de DFS
BFS
Análisis de BFS: algoritmo de búsqueda en anchura
Programando BFS con Python
Minimum Knights Moves (movimientos de caballo en ajedrez): análisis del problema
Solución de Minimum Knights Moves
Playground: Minimum Knights Moves
Programando Minimum Knights Moves con Python
Rotting Oranges: análisis del problema
Solución de Rotting Oranges
Playground: Rotting Oranges
Rotting Oranges con Java
Shortest Bridge Between Islands: análisis del problema
Solución de Shortest Bridge Between Islands
Playground: Shortest Bridge Between Islands
Programando Shortest Bridge Between Islands con Python
Ejercicios recomendados de BFS
Ejercicios resueltos de BFS
Backtrack
Algoritmo de Backtrack
Letter Combinations of a Phone Number: análisis del problema
Solución de Letter Combinations of a Phone Number
Programando Letter Combinations of a Phone Number con C++
Playground: Letter Combinations of a Phone Number
Restore IP Addresses: análisis del problema
Programando Restore IP Addresses con C++
Playground: Restore IP Addresses
Word Search: análisis del problema
Solución de Word Search
Playgrund: Word Search
Programando Word Search JavaScript
Reto: N Queens Puzzle
Ejercicios recomendados de Backtrack
Ejercicios resueltos de Backtrack
Próximos pasos
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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.
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:
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:
The selection of BFS as a search algorithm is particularly useful in several specific scenarios:
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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