Define the matrices: T [3 2; 0 -8; -4 3]; A = [diag(-1:2:3) T; -6 4 32 1]. Perform the following operations on the matrix A: (1)extract a vector consisting of the second and third elements of the third row (2)find the minimum of the second column (3)find the maximum of the third row %3D (4)compute the sum of the second column (5)extract the submatrix consisting of the first and second rows and all columns (6)extract the submatrix consisting of the first and third rows and the 3rd, 4th and 5th columns (7)compute the total sum of the 1st and 2nd rows (8)add 3 to al elements of the 2nd and 3rd columns

Define the matrices: T [3 2; 0 -8; -4 3]; A = [diag(-1:2:3) T; -6 4 32 1]. Perform the following operations on the matrix A: (1)extract a vector consisting of the second and third elements of the third row (2)find the minimum of the second column (3)find the maximum of the third row %3D (4)compute the sum of the second column (5)extract the submatrix consisting of the first and second rows and all columns (6)extract the submatrix consisting of the first and third rows and the 3rd, 4th and 5th columns (7)compute the total sum of the 1st and 2nd rows (8)add 3 to al elements of the 2nd and 3rd columns

C++ for Engineers and Scientists

4th Edition

ISBN:9781133187844

Author:Bronson, Gary J.

Publisher:Bronson, Gary J.

Chapter13: Structures

Section: Chapter Questions

Problem 4PP

See similar textbooks

Similar questions

(Sparse matrix–vector product) Recall from Section 3.4.2 that a matrix is said to be sparse if most of its entries are zero. More formally, assume a m × n matrix A has sparsity coefficient γ(A) ≪ 1, where γ(A) ≐ d(A)/s(A), d(A) is the number of nonzero elements in A, and s(A) is the size of A (in this case, s(A) = mn). 1. Evaluate the number of operations (multiplications and additions) that are required to form the matrix– vector product Ax, for any given vector x ∈ Rn and generic, non-sparse A. Show that this number is reduced by a factor γ(A), if A is sparse. 2. Now assume that A is not sparse, but is a rank-one modification of a sparse matrix. That is, A is of the form Ã + uv⊤, where Ã ∈ Rm,n is sparse, and u ∈ Rm, v ∈ Rm are given. Devise a method to compute the matrix–vector product Ax that exploits sparsity.
Problem 1) Change the diagonal elements of a 3X3 matrix to 1. Diagonal elements of the example matrix shown here are 10, 8, 2 10 -4 0 7 8 3 0 0 2 Read the given values from input (cin) using for loops and then write your code to solve the diagonal.
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.
Solving system of linear equations with two variables using Cramer’s rule. In mathematics the coefficientsof linear equations are represented in the form of matrices. A matrix is a two-dimensional array inprogramming. The steps of Cramer’s rule to solve the system of equations are:Step 1: Determining the coefficient matrix from linear equations e.g. a1x+b1y=d1a2x+b2y=d2Coefficient matrix: D = Step 2: Determining the constant column C = Step 3: Finding determinant |D| = (a1*b2) - (b1*a2) and checking if |D| is not equal to zero then do thefollowing: 1. Determining X-matrix: DX = The coefficients of the x−column are replaced by the constant column a1 b1a2 b2d1d2 d1 b1d2 b2 2. Determining Y-matrix: Dy = The coefficients of the Y−column are replaced by the constant column3. To solve for x: x=|DX|/|D|4. To solve for y: y=|Dy|/|D|Note that if |D|=0 then the matrix is singular and solving the system of equationis not possible. You’re required to automate all these steps by writing a program…
Consider a vector ('L', 'M', 'M', 'L', 'H', 'M', 'H', 'H'), where H stands for ‘high’, L stands for ‘low’, and M for ‘medium’ in rstudio. a. Convert this vector into an unordered factor. b. Convert this vector into an ordered factor with Low < Medium < High.
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…
Given a 2-D square matrix: ant mat{3}[{3]={{1, 2 3} {4,586}, {7.8.9FF Write a function transpose which Create a 3*3 matrix trans and store the transpose of given matrix in it.And prxnt the ' transpose, IN C++.
write a C++ program to Given a matrix of dimension m*n where each cell in the matrix can have values 0, 1 or 2 whichhas the following meaning:0: Empty cell1: Cells have fresh oranges2: Cells have rotten orangesSo we have to determine what is the minimum time required so that all the oranges becomerotten. A rotten orange at index [i,j] can rot other fresh orange at indexes [i-1,j], [i+1,j], [i,j-1],[i,j+1] (up, down, left and right). If it is impossible to rot every orange then simply return -1.Examples:Input: arr[][C] = { {2, 1, 0, 2, 1},{1, 0, 1, 2, 1},{1, 0, 0, 2, 1}};Output:All oranges can become rotten in 2 time frames.Input: arr[][C] = { {2, 1, 0, 2, 1},Tahir Iqbal Department of Computer Sciences. BULC{0, 0, 1, 2, 1},{1, 0, 0, 2, 1}};Output:All oranges cannot be rotten.Below is algorithm.1) Create an empty Q.2) Find all rotten oranges and enqueue them to Q. Also enqueuea delimiter to indicate beginning of next time frame.3) While Q is not empty do following3.a) While delimiter in…
Construct 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.
Type in Latex **Problem**. Let $$A = \begin{bmatrix} .5 & .2 & .3 \\ .3 & .8 & .3 \\ .2 & 0 & .4 \end{bmatrix}.$$ This matrix is an example of a **stochastic matrix**: its column sums are all equal to 1. The vectors $$\mathbf{v}_1 = \begin{bmatrix} .3 \\ .6 \\ .1 \end{bmatrix}, \mathbf{v}_2 = \begin{bmatrix} 1 \\ -3 \\ 2 \end{bmatrix}, \mathbf{v}_3 = \begin{bmatrix} -1 \\ 0 \\ 1\end{bmatrix}$$ are all eigenvectors of $A$. * Compute $\left[\begin{array}{rrr} 1 & 1 & 1 \end{array}\right]\cdot\mathbf{x}_0$ and deduce that $c_1 = 1$.* Finally, let $\mathbf{x}_k = A^k \mathbf{x}_0$. Show that $\mathbf{x}_k \longrightarrow \mathbf{v}_1$ as $k$ goes to infinity. (The vector $\mathbf{v}_1$ is called a **steady-state vector** for $A.$) **Solution**. To prove that $c_1 = 1$, we first left-multiply both sides of the above equation by $[1 \, 1\, 1]$ and then simplify both sides:$$\begin{aligned}[1 \, 1\, 1]\mathbf{x}_0 &= [1 \, 1\, 1](c_1\mathbf{v}_1 +…
Computer Science Given an N x N matrix M with binary entries i.e every entry is either 1 or 0. You are told that every row and every column is sorted in increasing order. You are required to output a pair (i,j) with 1 <= i and j <= n corresponding to the entry of the matrix satisfying Mij = 1 and Mrs = 0 for all 1 <= r <= i and 1 <= s <= j except for Mij Informally this includes the entry of M = 1 and is closest to the top left corner. for example: M = [ 0 0 0 1 0 0 1 1 0 0 1 1 0 0 1 1] output is (2,3) or (1,4) M = [ 0 1 1 1 1 1 1 1 1] output could be (1,2) or (2,1) Design a divide and conquer algorithm, explain correctness and runtime of the algorithm.
Matlab screenshot - If x=[ 2 6 12; 15 6 3; 10 11 1], then a) replace the first row elements of matrix x with its average value. b) reshape this matrix into row vector.