faster than computing both A and AB. 13. Suppose AB = AC, where B and C are n x p matrices and A is invertible. Show that B = C. Is this true, in general, when A is not invertible?
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- Array P = [40, 30, 25, 10, 35, 5, 20] Suppose the dimension of 6 matrices (A1, A2 … A6) are given by array P A1 is a P[0] x P[1] matrix A2 is a P[1] x P[2] matrix . . . A6 is a P[5] x P[6] a) Find the minimum number of scalar multiplications necessary to calculate the product of all the 6 matrices (A1.A2.A3.A4.A5.A6) and show the parenthesization for this multiplication. • Solve the problem manually (you need not to write any code) using bottom-up tabulation approach. Compute and show the ‘m’ matrix and ‘s’ matrix to solve your problem.17. Let A and B be two n × n matrices. Show that a) (A + B)^t = A^t + B^t . b) (AB)^t = B^t A^t . If A and B are n × n matrices with AB = BA = In, then B is called the inverse of A (this terminology is appropriate because such a matrix B is unique) and A is said to be invertible. The notation B = A^(−1) denotes that B is the inverse of A.Matrix multiplication plays an important role in a number of applications. Two matrices can only be multiplied if the number of columns of the fi rst matrix is equal to the number of rows in the second.Let’s assume we have an m × n matrix A and we want to multiply it by an n × p matrix B. We can express their product as an m × p matrix denoted by AB (or A ⋅ B). If we assign C = AB, and ci,j denotes the entry in C at position (i, j), then for each element i and j with 1 ≤ i ≤ m and 1 ≤ j ≤ p. Now we want to see if we can parallelize the computation of C. Assume that matrices are laid out in memory sequentially as follows: a1,1, a2,1, a3,1, a4,1, ..., etc.Assume that we are going to compute C on both a single core shared memory machine and a 4-core shared-memory machine. Compute the speedup we would expect to obtain on the 4-core machine, ignoring any memory issues.Repeat above Exercise, assuming that updates to C incur a cache miss due to false sharing when consecutive elements are in a…
- Write a report in which you discuss and compare your Gauss elimination and Gauss-Jordan programs. Gauss elimination #include<stdio.h> int main ( ) { int i, j, k, n; float A[20] [20], c, x [10], sum=0.0; printf("\Enter the order of matrix: "); Sacnf("%d,&n); prinf("\n Enter the elements of augmented matrix row-wise: \n\n"); for(i =1; i<=n; i++) { for(j=1; j<(n+1); j++) { printf("A[%d][%d] : ", i, j); scanf("%f", [i][j]); } } for(j=1; j<n; j++) { for(i=1; j<n; i++) { if(i>j) { c=A[i][j]/A[j][j]; for(k=1; k<=n+!; k++) { A[i][k] = A[i][k]-c*A[j][k]; } } } } x[n]= A[n] [n+1]/A[n][n]; for(i=n-1; i>=1; i--) { sum=0;…1. When dealing with large dense symmetric matrix it is recommendedthat to either store upper right or lower left entries only to avoidredundancy and thereby saving a lot of space (particularly when thematrix elements are of double type or any other user-defined datatype).Let us assume that we have decided to store only the upper right entriesof a matrix inside an 1D array in row-major arrangement.1. Design an algorithm for accepting a symmetric matrix from the userand printing the same matrix.2. Design an algorithm for multiplying a given symmetric matrix storedin 1D array with a compatible vector.2. A doubly ended queue - deque (Not to be confused with dequeueoperation) is a data structure consisting of a list of items on which thefollowing operations are possible:AddFront(x): Insert item x on the front end of the deque.DelFront(): Remove the front item from the deque and return it.AddLast(x): Insert item x on the rear end of the deque.DelLast(): Remove the rear item from the deque…When dealing with large dense symmetric matrix it is recommended that to either store upper right or lower left entries only to avoid redundancy and thereby saving a lot of space (particularly when the matrix elements are of double type or any other user-defined datatype). Let us assume that we have decided to store only the upper right entries of a matrix inside a 1D array in the row-major arrangement. 1. Design an algorithm for accepting a symmetric matrix from the user and printing the same matrix. 2. Design an algorithm for multiplying a given symmetric matrix stored in a 1D array with a compatible vector.
- Search a sorted matrix: The input consists of a real number x and a matrix A[1..n, 1..m] of nm real values, where A[i, 1..m] and A[1..n, j] are the rows and columns, respectively, of the matrix. The objective is to either state that all of the members of A are greater than x or to locate the largest array entry A[i, j] that is less than or equal to x. Create and evaluate an iterative approach for this issue that looks at the least amount of matrix entries feasible. If you think the issue can be resolved with a straightforward binary search, think twice.Generate random matrices of size n × n where n = 100, 200, . . . , 1000.Also generate a random b ∈ Rnfor each case. Each number must beof the form m.dddd (Example : 4.5444) which means it has 5 Significant digits in total. Perform Gaussian elimination with and withoutpartial pivoting for each n value (10 cases) above. Report the numberof additions, divisions and multiplications for each case in the form ofa table. No need of the code and the matrices / vectors. Deliverable(s): Two tabular columns indicating the number of additions, multiplications and divisions for each value of n, for with andwithout pivoting in PythonConstruct a square matrix with NN rows and NN columns consisting of nonnegative integers from 00 to 10^{18}1018, such that its determinant is equal to 11, and there are exactly A_iAi odd numbers in the ii-th row for each ii from 11 to NN, or report there isn't such a matrix. Standard input The first line contains a single integer NN. Each of the next NN lines contains a single integer A_iAi. Standard output If there is no solution, output \text{-}1-1. Otherwise, print NN lines, each consisting of NN integers, representing the values of the constructed matrix. If there are multiple solutions, print any. Constraints and notes 2 \le N \le 502≤N≤50 1 \leq A_i \leq N1≤Ai≤N For 40\%40% of the test files, N \le 17N≤17.
- Let y be a column vector: y = [1, 2, 3, 4, 5, 6] so that y.shape = (6,1). Reshape the vector into a matrix z using the numpy.array.reshape and (numpy.array.transpose if necessary) to form a new matrix z whose first column is [1, 2, 3], and whose second column is [4, 5, 6]. Print out the resulting array z.Dont the matrix V,P dont need any value?23( The matrix below is stored in the Row-Major order in the linear memory, so in what order are the numbers stored. a. 8, 4, 3, 7, 9, -8, 5, 6, -2. b. 8, 6, -8, 7, 3, 4, -5, 9, -2. c. 8, -5, 7, 4, 6, 9, 3, -2 , -8. d. 8, 4, 3, -5, 6, -2, 7, 9, -8.