Using Java, create and implement an iterative improvement algorithm that will find the approximation of a vertex in a parabola. The x-coordinate needs to be within 0.01 of the actual value. The algorithm must work for any quadratic function. Sample Run: Enter the coefficients for the parabola ax^2+bx+c: 2, 3, 1 The vertex for the parabola is approximately: (-0.75, -0.125)

Using Java, create and implement an iterative improvement algorithm that will find the approximation of a vertex in a parabola. The x-coordinate needs to be within 0.01 of the actual value. The algorithm must work for any quadratic function. Sample Run: Enter the coefficients for the parabola ax^2+bx+c: 2, 3, 1 The vertex for the parabola is approximately: (-0.75, -0.125)

C++ Programming: From Problem Analysis to Program Design

8th Edition

ISBN:9781337102087

Author:D. S. Malik

Publisher:D. S. Malik

Chapter8: Arrays And Strings

Section: Chapter Questions

Problem 24PE

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w describe the Python code for the 2-approximation algorithm. The main part of the code deals with deleting the randomly selected edge from the graph along with its neighboring edges. Input to the procedure VC_Approx is the incidence matrix B(G) of the graph G. The unmarked edges are held in the list edges and the endpoints of a selected edge are included in the list V C at each iteration. The incidence matrix B of the graph is input and we find adjacent edges to the matched edge by iterating through rows of B. Each found edge is deleted from the list edges and main while loop iterates until this list becomes empty. We check adjacent edges to the matched edge from this matrix by deleting adjacent edges at each endpoint.
Write pseudocode to find all pairs shortest paths using the technique used in Bellman-Ford's algorithm so that it will produce the same matrices like Floyd-Warshall algorithm produces. Also provide the algorithm to print the paths for a source vertex and a destination vertex. Describe the properties of the algorithm you provide and the run time for your algorithm in detail.
describe the 2-approximation algorithm's Python source code. The majority of the code is concerned with removing the randomly chosen edge and its surrounding edges from the graph. The incidence matrix B(G) from the graph G is the input to the procedure VC Approx. Every iteration, the endpoints of a chosen edge are added to the list V C and the unmarked edges are kept in the list edges. We input the graph's incidence matrix B and then iterate through the rows of B to find edges that are near the matched edge. The main while loop iterates until this list is empty after each found edge is removed from the list edges.
"Dijkstra's single-source shortest path algorithm returns a results grid that contains the lengths of the shortest paths from a given vertex [the source vertex] to the other vertices reachable from it. Develop a pseudocode algorithm that uses the results grid to build and return the actual [shortest] path, as a list of vertices, from the source vertex to a given [target] vertex. (Hint: This algorithm starts with a given vertex [the target vertex] in the grid's first column and gathers ancestor [parent] vertices, until the source vertex is reached.)" *For your algorithm, assume that grid is the name of the results grid produced by Dijkstra's single-source shortest path algorithm. *Each vertex is identified by its label/name, which is in column 1 of grid. *As the first step of your algorithm, find the name of the source vertex. *Next, get the name of the target vertex from the user. Pseudocode should avoid details through broad-stroke statements. However, it must give enough information…
Given below is the implementation of the bellman ford and dijkstras algorithm. Please complete the code for the time_shortest_path_algs() function according to the instructions in the 1st screenshot provided. Done in python 3.10 or later please def bellman_ford(self,s) : """Bellman Ford Algorithm for single source shortest path. Keyword Arguments: s - The source vertex. """ distances = {v: float('inf') for v in self.adjacency_list} distances[s] = 0 parents = {v: None for v in self.adjacency_list} for _ in range(len(self.adjacency_list) - 1): for from_vertex in self.adjacency_list: for to_vertex in self.adjacency_list[from_vertex]: if distances[from_vertex] + self.weights[(from_vertex, to_vertex)] < distances[to_vertex]: distances[to_vertex] = distances[from_vertex] + self.weights[(from_vertex, to_vertex)] parents[to_vertex] =…
6. In Java create an algorithm for an undirected graph with n vertices and m edges that...- Takes as a parameter some integer k- Returns the maximum induced subgraph where each vertex of that subgraph has a degree greater than or equal to k (or returns null if no such subgraph exists)- Operates in O(n + m) time
5. Given an undirected graph with n vertices and m edges, find an O(n+m) time algorithm that determines whether it is possible to color all the vertices red and blue such that every edge is between a red vertex and blue vertex. If such a coloring exists, your algorithm should produce one.
Hi I need help writing a java program that implements Prim's algorithm. The imput of the program is given by the scanner with an undirected graph. First input line is the number of vertices, second input is the number of undirected edges. After that the input is a matrix of 3 by the number of undirected edges. The matrix firt 2 column contain the end points and the third column contains the weight . The output of the program is an integer representing the sum of all the edges that make up the minimum spanning tre. I have attach a picture for farther details. THANKS
Please do not give solution in image formate thanku. Write a Python code for the following scenario : 1: Use Breadth First Search and Recursive Best First Search and implement the algorithms in Python. The program should be able to dynamically take in start and goal nodes from the user at run time. Compare to interpret the results in terms of the algorithm working, performance & shortest path if obtained relevant to the given problem 2: Print the minimum connections that keep the whole network up and running. For each incremental depth limit, the corresponding node generating sequence should be legibly printed in the result Consider Heuristic Value as : The edge cost is an approximation towards the transmission cost between any pair of nodes. For heuristic design, consider all the possible paths between any arbitrary node n to the goal node. The average of the total transmission cost across all these paths is the heuristic value h(n) Consider the following undirected graph…
Specifications: You will create an implementation of this algorithm. Your driver program should provide a graph and a source vertex in the graph. Your implementation should use Dijkstra's Algorithm to determine the shortest path using adjacency matrix representation. Specifically, given a graph and a source vertex in the graph, find the shortest paths from source to all vertices in the given graph, using Dijkstra's Algorithm.
The coordinates of a polygon can be represented as a list of tuples: [(x1, y1), (x2, y2), .., (xn, yn)], where (x1, y1), ., (xn, yn) are points of the polygon in a counterclockwise order. Find the area of such a polygon using using shoelace formula. It is no longer required to take absolute value. п-1 п—1 A = > xi+1Yi ) – x1Yn i=1 i=1 1 x142 + x2Y3 + + xn-1Yn + xnY1 - x2y1 – X3Y2 – ··- xn Yn-1 – x1Yn| >>> area ([(0,0), (1,0), (0,1)]) 0.5 >>> area ([(0,0), (1,0),(1,1),(0,1)]) 1.0 >> area([(0,0),(2,0), (2,2),(1,2), (0,1)]) 3.5 Write the function area(C) to find the area of a triangle with vertices at c = [(x1, y1), (x2, y2),..., (xn, yn)].
USE PYTHON and ONLY CAN USE THE NUMPY IMPORT In this problem you will implement a function called pagerank which will take as input a numpy array TM, which will represent the transition matrix of a directed graph, and a positive integer n. The output will be a numpy array which gives the page ranks of each vertex in the graph represented by TM. You will iterate the update process n times. THE CODE STARTS imports numpy as np def pagerank(TM, n): C = np.array([[0,1,1],[1,1,1],[0,0,1]]) ------- enter your code -----