Database System Concepts
Database System Concepts
7th Edition
ISBN: 9780078022159
Author: Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher: McGraw-Hill Education
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**REQUIREMENTS:**

1. Using Python, write a program called `dfs-stack.py` to implement Algorithm 2.3 (p. 49): Graph depth-first search (DFS) with a stack.
2. Do not use an adjacency list, as indicated in Algorithm 2.3. Instead, use an adjacency matrix (i.e., a two-dimensional array or, in Python, a list of lists).
3. You may use any list method you wish (e.g., append, pop, etc.).

**IMPLEMENTATION DETAILS:**

1. Based on the requirements above and the implementation details in this section, first develop an algorithmic solution using pseudocode. This includes both your logic (in pseudocode) and the logic presented in the pseudocode indicated in Algorithm 2.3.
2. Include your name and the Certificate of Authenticity as comments at the very beginning of your Python code. If you collaborated with others, include their names as well.
3. Begin by prompting the user for the number of vertices, \( V \), in the graph, \( G \).
4. Represent the graph \( G \) using an adjacency matrix— a square matrix with a row and a column for each vertex. Create a matrix \( M \) that consists of a \( V \times V \) two-dimensional array (in Python, a list of lists). Initialize each element of the matrix to zero.
5. Prompt the user to indicate which elements in the matrix should be assigned the value of 1 (i.e., information about vertex connections). Each element in the matrix is the intersection of a row and a column.
6. Steps 3, 4, and 5 should create an adjacency matrix representation of the graph, \( G \). Print the newly-created adjacency matrix on the screen.
7. Prompt the user to specify a node—i.e., the starting vertex in \( G \).
8. Proceed with the implementation of Algorithm 2.3, with the following enhancements:
   - Use the newly-created adjacency matrix instead of an adjacency list.
   - Immediately following line 5 (but before line 6) of Algorithm 2.3, print the values currently on the stack.
   - At the end of the while block, print the values currently on the stack.

In effect, the result of step 9 above should display the stack evolution for your implementation of the DFS algorithm on graph \( G \).
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Transcribed Image Text:**REQUIREMENTS:** 1. Using Python, write a program called `dfs-stack.py` to implement Algorithm 2.3 (p. 49): Graph depth-first search (DFS) with a stack. 2. Do not use an adjacency list, as indicated in Algorithm 2.3. Instead, use an adjacency matrix (i.e., a two-dimensional array or, in Python, a list of lists). 3. You may use any list method you wish (e.g., append, pop, etc.). **IMPLEMENTATION DETAILS:** 1. Based on the requirements above and the implementation details in this section, first develop an algorithmic solution using pseudocode. This includes both your logic (in pseudocode) and the logic presented in the pseudocode indicated in Algorithm 2.3. 2. Include your name and the Certificate of Authenticity as comments at the very beginning of your Python code. If you collaborated with others, include their names as well. 3. Begin by prompting the user for the number of vertices, \( V \), in the graph, \( G \). 4. Represent the graph \( G \) using an adjacency matrix— a square matrix with a row and a column for each vertex. Create a matrix \( M \) that consists of a \( V \times V \) two-dimensional array (in Python, a list of lists). Initialize each element of the matrix to zero. 5. Prompt the user to indicate which elements in the matrix should be assigned the value of 1 (i.e., information about vertex connections). Each element in the matrix is the intersection of a row and a column. 6. Steps 3, 4, and 5 should create an adjacency matrix representation of the graph, \( G \). Print the newly-created adjacency matrix on the screen. 7. Prompt the user to specify a node—i.e., the starting vertex in \( G \). 8. Proceed with the implementation of Algorithm 2.3, with the following enhancements: - Use the newly-created adjacency matrix instead of an adjacency list. - Immediately following line 5 (but before line 6) of Algorithm 2.3, print the values currently on the stack. - At the end of the while block, print the values currently on the stack. In effect, the result of step 9 above should display the stack evolution for your implementation of the DFS algorithm on graph \( G \).
### Algorithm 2.3: Graph Depth-First Search with a Stack

**StackDFS**(G, node) → visited

**Input**: 
- \( G = (V, E) \), a graph
- *node*, the starting vertex in \( G \)

**Output**: 
- *visited*, an array of size \(|V|\) such that *visited[i]* is true if we have visited node *i*, false otherwise

1. \( S \leftarrow \) CreateStack()
2. *visited* \( \leftarrow \) CreateArray(\(|V|\))
3. for \( i \leftarrow 0 \) to \(|V|\) do
4.   *visited[i]* \( \leftarrow \) false
5. Push(\( S, \) *node*)
6. while not IsStackEmpty(\( S \)) do
7.   \( c \leftarrow \) Pop(\( S \))
8.   *visited[c]* \( \leftarrow \) true
9.   foreach \( v \) in AdjacencyList(\( G, c \)) do
10.    if not *visited[v]* then
11.     Push(\( S, \) *v*)
12. return *visited*

This pseudocode describes a method for performing a Depth-First Search (DFS) on a graph using a stack data structure. The algorithm initializes a stack and a 'visited' array. It then iteratively explores the graph by marking nodes as visited and exploring their adjacent nodes. Expanding the search in this stack-based iterative manner avoids the pitfalls of recursion in environments with limited stack size.
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Transcribed Image Text:### Algorithm 2.3: Graph Depth-First Search with a Stack **StackDFS**(G, node) → visited **Input**: - \( G = (V, E) \), a graph - *node*, the starting vertex in \( G \) **Output**: - *visited*, an array of size \(|V|\) such that *visited[i]* is true if we have visited node *i*, false otherwise 1. \( S \leftarrow \) CreateStack() 2. *visited* \( \leftarrow \) CreateArray(\(|V|\)) 3. for \( i \leftarrow 0 \) to \(|V|\) do 4.   *visited[i]* \( \leftarrow \) false 5. Push(\( S, \) *node*) 6. while not IsStackEmpty(\( S \)) do 7.   \( c \leftarrow \) Pop(\( S \)) 8.   *visited[c]* \( \leftarrow \) true 9.   foreach \( v \) in AdjacencyList(\( G, c \)) do 10.    if not *visited[v]* then 11.     Push(\( S, \) *v*) 12. return *visited* This pseudocode describes a method for performing a Depth-First Search (DFS) on a graph using a stack data structure. The algorithm initializes a stack and a 'visited' array. It then iteratively explores the graph by marking nodes as visited and exploring their adjacent nodes. Expanding the search in this stack-based iterative manner avoids the pitfalls of recursion in environments with limited stack size.
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