Course Content
Algorithms and Data Structures Overview
Algorithms and Data Structures Overview
Challenge: Graph Traversal Algorithms
Graph traversal algorithms are used to systematically visit all the nodes in a graph. Two commonly used algorithms for graph traversal are Depth-First Search (DFS) and Breadth-First Search (BFS).
DFS algorithm
DFS explores as far as possible along each branch before backtracking. It uses a stack to keep track of the nodes to visit next. This is because DFS explores as far as possible along each branch before backtracking, and the stack allows it to remember the path from the starting node to the current node, ensuring that it can backtrack to the previous node and explore other branches when necessary.
- Start at a vertex and mark it as visited;
- Explore all adjacent, unvisited vertices recursively or using a stack;
- Repeat the process until all vertices are visited.
BFS algorithm
Breadth-First Search (BFS) systematically explores a graph by visiting all the nodes at the current depth level before moving on to the nodes at the next depth level. It utilizes a queue to manage the exploration order, ensuring that nodes are visited in the order they were discovered and allowing it to explore the graph layer by layer.
- Start at a vertex and mark it as visited;
- Enqueue the vertex into a queue;
- Dequeue a vertex from the queue and explore all its adjacent, unvisited vertices;
- Enqueue those vertices into the queue;
- Repeat steps 3-4 until the queue is empty.
Task
Your task is to implement BFS and DFS algorithms by filling the gaps in the bfs() and dfs() functions.
In the bfs() function, we need to implement Breadth-First Search (BFS) algorithm, which utilizes a queue to traverse the graph. This means we should dequeue the first element from the array of vertices as we explore the graph.
Conversely, in the dfs() function, we need to implement Depth-First Search (DFS) algorithm, which employs a stack to traverse the graph. As a result, we should pop the last element from the array of vertices while navigating through the graph.
Task
Your task is to implement BFS and DFS algorithms by filling the gaps in the bfs() and dfs() functions.
In the bfs() function, we need to implement Breadth-First Search (BFS) algorithm, which utilizes a queue to traverse the graph. This means we should dequeue the first element from the array of vertices as we explore the graph.
Conversely, in the dfs() function, we need to implement Depth-First Search (DFS) algorithm, which employs a stack to traverse the graph. As a result, we should pop the last element from the array of vertices while navigating through the graph.
Switch to desktop for real-world practiceContinue from where you are using one of the options belowEverything was clear?
Challenge: Graph Traversal Algorithms
Graph traversal algorithms are used to systematically visit all the nodes in a graph. Two commonly used algorithms for graph traversal are Depth-First Search (DFS) and Breadth-First Search (BFS).
DFS algorithm
DFS explores as far as possible along each branch before backtracking. It uses a stack to keep track of the nodes to visit next. This is because DFS explores as far as possible along each branch before backtracking, and the stack allows it to remember the path from the starting node to the current node, ensuring that it can backtrack to the previous node and explore other branches when necessary.
- Start at a vertex and mark it as visited;
- Explore all adjacent, unvisited vertices recursively or using a stack;
- Repeat the process until all vertices are visited.
BFS algorithm
Breadth-First Search (BFS) systematically explores a graph by visiting all the nodes at the current depth level before moving on to the nodes at the next depth level. It utilizes a queue to manage the exploration order, ensuring that nodes are visited in the order they were discovered and allowing it to explore the graph layer by layer.
- Start at a vertex and mark it as visited;
- Enqueue the vertex into a queue;
- Dequeue a vertex from the queue and explore all its adjacent, unvisited vertices;
- Enqueue those vertices into the queue;
- Repeat steps 3-4 until the queue is empty.
Task
Your task is to implement BFS and DFS algorithms by filling the gaps in the bfs() and dfs() functions.
In the bfs() function, we need to implement Breadth-First Search (BFS) algorithm, which utilizes a queue to traverse the graph. This means we should dequeue the first element from the array of vertices as we explore the graph.
Conversely, in the dfs() function, we need to implement Depth-First Search (DFS) algorithm, which employs a stack to traverse the graph. As a result, we should pop the last element from the array of vertices while navigating through the graph.
Task
Your task is to implement BFS and DFS algorithms by filling the gaps in the bfs() and dfs() functions.
In the bfs() function, we need to implement Breadth-First Search (BFS) algorithm, which utilizes a queue to traverse the graph. This means we should dequeue the first element from the array of vertices as we explore the graph.
Conversely, in the dfs() function, we need to implement Depth-First Search (DFS) algorithm, which employs a stack to traverse the graph. As a result, we should pop the last element from the array of vertices while navigating through the graph.
Switch to desktop for real-world practiceContinue from where you are using one of the options belowEverything was clear?
Challenge: Graph Traversal Algorithms
Graph traversal algorithms are used to systematically visit all the nodes in a graph. Two commonly used algorithms for graph traversal are Depth-First Search (DFS) and Breadth-First Search (BFS).
DFS algorithm
DFS explores as far as possible along each branch before backtracking. It uses a stack to keep track of the nodes to visit next. This is because DFS explores as far as possible along each branch before backtracking, and the stack allows it to remember the path from the starting node to the current node, ensuring that it can backtrack to the previous node and explore other branches when necessary.
- Start at a vertex and mark it as visited;
- Explore all adjacent, unvisited vertices recursively or using a stack;
- Repeat the process until all vertices are visited.
BFS algorithm
Breadth-First Search (BFS) systematically explores a graph by visiting all the nodes at the current depth level before moving on to the nodes at the next depth level. It utilizes a queue to manage the exploration order, ensuring that nodes are visited in the order they were discovered and allowing it to explore the graph layer by layer.
- Start at a vertex and mark it as visited;
- Enqueue the vertex into a queue;
- Dequeue a vertex from the queue and explore all its adjacent, unvisited vertices;
- Enqueue those vertices into the queue;
- Repeat steps 3-4 until the queue is empty.
Task
Your task is to implement BFS and DFS algorithms by filling the gaps in the bfs() and dfs() functions.
In the bfs() function, we need to implement Breadth-First Search (BFS) algorithm, which utilizes a queue to traverse the graph. This means we should dequeue the first element from the array of vertices as we explore the graph.
Conversely, in the dfs() function, we need to implement Depth-First Search (DFS) algorithm, which employs a stack to traverse the graph. As a result, we should pop the last element from the array of vertices while navigating through the graph.
Task
Your task is to implement BFS and DFS algorithms by filling the gaps in the bfs() and dfs() functions.
In the bfs() function, we need to implement Breadth-First Search (BFS) algorithm, which utilizes a queue to traverse the graph. This means we should dequeue the first element from the array of vertices as we explore the graph.
Conversely, in the dfs() function, we need to implement Depth-First Search (DFS) algorithm, which employs a stack to traverse the graph. As a result, we should pop the last element from the array of vertices while navigating through the graph.
Switch to desktop for real-world practiceContinue from where you are using one of the options belowEverything was clear?
Graph traversal algorithms are used to systematically visit all the nodes in a graph. Two commonly used algorithms for graph traversal are Depth-First Search (DFS) and Breadth-First Search (BFS).
DFS algorithm
DFS explores as far as possible along each branch before backtracking. It uses a stack to keep track of the nodes to visit next. This is because DFS explores as far as possible along each branch before backtracking, and the stack allows it to remember the path from the starting node to the current node, ensuring that it can backtrack to the previous node and explore other branches when necessary.
- Start at a vertex and mark it as visited;
- Explore all adjacent, unvisited vertices recursively or using a stack;
- Repeat the process until all vertices are visited.
BFS algorithm
Breadth-First Search (BFS) systematically explores a graph by visiting all the nodes at the current depth level before moving on to the nodes at the next depth level. It utilizes a queue to manage the exploration order, ensuring that nodes are visited in the order they were discovered and allowing it to explore the graph layer by layer.
- Start at a vertex and mark it as visited;
- Enqueue the vertex into a queue;
- Dequeue a vertex from the queue and explore all its adjacent, unvisited vertices;
- Enqueue those vertices into the queue;
- Repeat steps 3-4 until the queue is empty.
Task
Your task is to implement BFS and DFS algorithms by filling the gaps in the bfs() and dfs() functions.
In the bfs() function, we need to implement Breadth-First Search (BFS) algorithm, which utilizes a queue to traverse the graph. This means we should dequeue the first element from the array of vertices as we explore the graph.
Conversely, in the dfs() function, we need to implement Depth-First Search (DFS) algorithm, which employs a stack to traverse the graph. As a result, we should pop the last element from the array of vertices while navigating through the graph.
Switch to desktop for real-world practiceContinue from where you are using one of the options belowSwitch to desktop for real-world practiceContinue from where you are using one of the options below