Consider the same maze shown above with the same start and goal nodes. Let the heuristic cost ofevery node to reach G be:Table ofheuristicvalueshh(n)A 5.1B 4.1C 3.9D 4E 2.2F 3.8G 0H 3.77K 6L 4M 0.5N 1.5P 1.8S 4.5List the vertices expanded by a A* search (enter the single letter label of a node).1. S2. B3.4.5.6.7.8.

During the process, the nodes S, B, F, K, J, L, M, and G expand. Here's how: Explanation:The A*…

Answered: Consider the same maze shown above with…

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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consider alphabetical order if there are more than option thank you.
Consider the graph below. Use Dijkstra's algorithm to find the shortest path from vertex A to vertexH. Write your answer a a sequence of nodes separated by commas (no blank spaces) starting with the source node: What's the weight of the shortest path? 15 10 8. 10 15 10 12 14
5. * Show that there is an algorithm to decide if a graph has a vertex cover of size k, with running time O(kn +20(k)). Hint: use the FPT algorithm discussed in class and the results of the previous problem.
Luby’s Algorithm: 1. Input: G = (V, E)2. Output: MIS I of G3. I ← ∅4. V ← V5. while V = ∅6. assign a random number r(v) to each vertex v ∈ V7. for all v ∈ V in parallel8. if r(v) is minimum amongst all neighbors9. I ← I ∪ {v}10. V ← V \ {v ∪ N(v)}Make Implementation of this algorithm in sequential form in Python. dont copy ans from bartleby its wrong
find the time efficiency using 5 steps plan: Graph Complete(A[0..n-1,0..n-1]) Input:Adjacency matrix (A[0..n-1,0..n-1]) output: 1(true) if n=1 return 1 else if not Graphcomplete (A[0..n-2,0..n-2]) return 0 else for j <--- 0 to n-2 do if A[n-1,j] =0 return 0 return 1
output ExampleInput:41 1 2 2 Output:72
Use of Matching for Edge ColouringEdges in a matching can share the same colour since matching in a graph is defined as the collection of non-adjacent edges. As a result, we can create an algorithm based on this idea using the below stages. 1. Input: G = (V, E)2. Output: Minimal edge coloring of G3. color ← 04. while G = ∅$5. find maximal matching M of G6. color all M vertices with color7. G ← G − M8. color ← color + 1implementation of Python
Luby’s Algorithm: 1. Input: G = (V, E)2. Output: MIS I of G3. I ← ∅4. V ← V5. while V = ∅6. assign a random number r(v) to each vertex v ∈ V7. for all v ∈ V in parallel8. if r(v) is minimum amongst all neighbors9. I ← I ∪ {v}10. V ← V \ {v ∪ N(v)}Make Implementation of this algorithm in sequential form in Python
Petermine the shortest path using Dijkstra's algorithm from node z to all other nodes in the network. You need to draw a table similar to the one shown in class and clearly illustrate all the steps. You will not get credit for just writing the final answer. 12 7 6 8. 4. 3.
[Item2] Instructions: Kindly provide the complete and correct solutions. I won't like it if it is incomplete and incorrect.
There are many applications of Shortest Path Algorithm. Consider the problem of solving a jumbled Rubik's Cube in the fewest number of moves. I claim that this problem can be solved using a Shortest Path Algorithm. Determine whether this statement is TRUE or FALSE. NOTE: if you want to check if this statement is TRUE, think about how the Rubik's Cube Problem can be represented as a graph. What are the vertices? Which pairs of vertices are connected with edges? What is your source vertex and what is your destination vertex? How would Dijkstra's Algorithm enable you to find the optimal sequence of moves to solve a jumbled cube in the fewest number of moves?
A. Let G be a graph on 17 vertices. We know that G contains at most one cycle. How many edges can G contain at the most? B. The expression r is written in post-order notation (also referred to as RPN). What value does r evaluate to? r = 1 6 7 + - 5 3 + 2 * +

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