Master-Level Programming Assignments with Expert Solutions
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Sample Master-Level Programming Assignment Questions and Solutions
Question 1: Implementing a Multi-Threaded Web Server in Java
Problem Statement: Design and implement a multi-threaded web server in Java that can handle multiple client requests concurrently. The server should listen on a specific port and respond with a simple HTML page when accessed.
Solution:
import java.io.*; import java.net.*; import java.util.concurrent.*; public class MultiThreadedServer { public static void main(String[] args) throws IOException { int port = 8080; ServerSocket serverSocket = new ServerSocket(port); ExecutorService pool = Executors.newFixedThreadPool(10); System.out.println("Server is running on port " + port); while (true) { Socket clientSocket = serverSocket.accept(); pool.execute(new ClientHandler(clientSocket)); } } } class ClientHandler implements Runnable { private Socket clientSocket; public ClientHandler(Socket socket) { this.clientSocket = socket; } public void run() { try { PrintWriter out = new PrintWriter(clientSocket.getOutputStream(), true); out.println("HTTP/1.1 200 OK\r\n" + "Content-Type: text/html\r\n\r\n" + "<html><body><h1>Welcome to My Server</h1></body></html>"); clientSocket.close(); } catch (IOException e) { e.printStackTrace(); } } }
This multi-threaded Java server effectively handles multiple client requests using an ExecutorService, ensuring efficient response handling.
Question 2: Implementing Dijkstra’s Algorithm in Python
Problem Statement: Write a Python program to implement Dijkstra’s shortest path algorithm for a given graph. The program should take a graph as an adjacency matrix and compute the shortest path from a given source node to all other nodes.
Solution:
import heapq def dijkstra(graph, start): priority_queue = [(0, start)] shortest_paths = {node: float('inf') for node in graph} shortest_paths[start] = 0 while priority_queue: current_distance, current_node = heapq.heappop(priority_queue) if current_distance > shortest_paths[current_node]: continue for neighbor, weight in graph[current_node].items(): distance = current_distance + weight if distance < shortest_paths[neighbor]: shortest_paths[neighbor] = distance heapq.heappush(priority_queue, (distance, neighbor)) return shortest_paths # Example Usage graph = { 'A': {'B': 1, 'C': 4}, 'B': {'A': 1, 'C': 2, 'D': 5}, 'C': {'A': 4, 'B': 2, 'D': 1}, 'D': {'B': 5, 'C': 1} } print(dijkstra(graph, 'A'))
This Python implementation of Dijkstra’s algorithm efficiently computes the shortest paths using a priority queue.
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