Build a solution for the 0-1 Knapsack problem using best-first search algorithm (Ch. 6.1.2). We have 5 items with price and weight (p/w): 1. $50/20 2. $30/5 3. $40/10 4. $10/5 5. $20/4 Limitation on the weight is: W = 21. Find a sequence of visited nodes, find a final solution. Paragraph く B I U く A ✓ Ev ►11 く 00 Lato (Recom... V 19px... v </> D + v ง རུཊ

Build a solution for the 0-1 Knapsack problem using best-first search algorithm (Ch. 6.1.2). We have 5 items with price and weight (p/w): 1. $50/20 2. $30/5 3. $40/10 4. $10/5 5. $20/4 Limitation on the weight is: W = 21. Find a sequence of visited nodes, find a final solution. Paragraph く B I U く A ✓ Ev ►11 く 00 Lato (Recom... V 19px... v </> D + v ง རུཊ

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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In the figure below there is a weighted graph, dots represent vertices, links represent edges, and numbers represent edge weights. S 2 1 2 1 2 3 T 1 1 2 4 (a) Find the shortest path from vertex S to vertex T, i.e., the path of minimum weight between S and T. (b) Find the minimum subgraph (set of edges) that connects all vertices in the graph and has the smallest total weight (sum of edge weights). 2. 3.
In a network of N nodes, how many iterations are required for Dijkstra's algorithm to completes its execution. Hint: Section 5.2.1 has the pseudo code for Dijkstra's link state algorithm that finds the shortest path from a source node to every node in the network. Professor Kurose shows the pseudo code in his video. The pseudo code in the text shows an initialization step and then a loop. How many times would the loop be executed for a network of N nodes? O N+1 times ON-1 times ON times O N² times
Build a solution for the 0-1 Knapsack problem using best-first search algorithm (Ch. 6.1.2). We have 5 items with price and weight (p/w): 1. $50/20 2. $30/5 3. $40/10 4. $10/5 5. $20/4 Limitation on the weight is: W = 21. Find a sequence of visited nodes, find a final solution. Paragraph く B I U く A ✓ Ev ►11 く 00 Lato (Recom... V 19px... v D + v ง རུཊ
Consider three variables (X, Y, and Z), and their values as in the following: X = {2,3,7}, Y = {3,5}, and Z = {7,2}. We want to find all combinations of X, Y, and Z such that X≠Y,Y≠Z, and X≠Z. Give the search space (i.e., the entire search tree). You can consider any order of variables and values. In the search tree, identify all the solutions.
Please implement the A* search algorithm in Python for the problem scenario below. Please explain your code and your approach. PROBLEM SCENARIO On holiday, a flight currently wants to travel to Bucharest from Arad. But there isno direct way to Bucharest from Arad. However, the cities are connected witheach other like a graph. The distance between the connected cities are given. Theflight wants to travel through the most optimal way. To find the optimal path totravel, another information is provided: the straight line distance between any cityand the final destination (Bucharest). Now apply A* search to determine the most optimal value for the route Arad toBucharest and help the flight. You have to use the straight line distance as theheuristic value for the cities. City Heuristic value City Heuristic valueArad 366 Mehadia 241Bucharest 0 Neamt 234Craiova 160 Oradea 380Eforie…
In this question you will explore Graph Colouring algorithms. Given a graph G, we say that G is k-colourable if every vertex of G can be assigned one of k colours so that for every pair u, v of adjacent vertices, u and v are assigned different colours. The chromatic number of a graph G, denoted by χ(G), is the smallest integer k for which graph G is k-colorable. To show that χ(G) = k, you must show that the graph is k-colourable and that the graph is not (k − 1)-colourable. Question: It is NP-complete to determine whether an arbitrary graph has chromatic number k, where k ≥ 3. However, determining whether an arbitrary graph has chromatic number 2 is in P. Given a graph G on n vertices, create an algorithm that will return TRUE if χ(G) = 2 and FALSE if χ(G) 6= 2. Clearly explain how your algorithm works, why it guarantees the correct output, and determine the running time of your algorithm.
Let T be a binary search tree, and let z be a key. Give an efficient algorithm for finding the smallest key y in T such that y > r. Note that r may or may not be in T. Explain why your algorithm has the running time it does.
Answer the given question with a proper explanation and step-by-step solution. You are asked to pick up a project on building highways to connect all cities in the country. The cost of building a highway between two cites i and j is c(i, j) > 0. If you were in charge from the beginning, this would have been a minimum spanning tree problem and could be solve easily with the algorithms covered in class. Since you pick it up halfway, however, some suboptimal choices have already been made by your predecessor. In other words, highways were already built between some pairs of cities. Design an algorithm to find a cost minimizing set of highways to built subject to the choices already made. Do not copy others.
Note: Your solution should have O(n) time complexity, where n is the number of elements in l, and O(1) additional space complexity, since this is what you would be asked to accomplish in an interview. Given a linked list l, reverse its nodes k at a time and return the modified list. k is a positive integer that is less than or equal to the length of l. If the number of nodes in the linked list is not a multiple of k, then the nodes that are left out at the end should remain as-is. You may not alter the values in the nodes - only the nodes themselves can be changed.
36. Let G be a simple graph on n vertices and has k components. Then the number m of edges of G satisfies n-k ≤m if G is a null graph. This statement is A. sometimes true B. always true C. never true D. Neither true nor false
Consider a graph and implement Breadth-first search, Uniform-cost search, Depth-first search, Depth-limited search, Iterative deepening depth-first search and Bidirectional search using your favorite programming language. Also draw and visualize the solution.
The clique problem is finding cliques in a diagram. A clique is a set of vertices that are adjacent to each other. The 4-clique is a set of four knots all connected. So, in this example of the 4-clique problem, we have a graph with 7 vertices. A brute force algorithm searched all possible combinations of four vertices and found a set that formed a clique. If you want to understand more about it, the problem (and if possible read on). Note that the clique problem is NP-complete, so deterministic search is not practical for large graph sizes. This makes it an ideal candidate for evolutionary exploration. In this problem, we have to assume that we are given the problem of implementing the 4-clique problem as an evolutionary algorithm for an arbitrary graph with an arbitrary number of vertices (an n-vertex graph). If 4 cliques are found, the algorithm succeeds. 1. Provide an algebraic expression, in terms of n, for the size of the phenotypic search space (the number of possible…