Introduction to mathematical programming
4th Edition
ISBN: 9780534359645
Author: Jeffrey B. Goldberg
Publisher: Cengage Learning
expand_more
expand_more
format_list_bulleted
Expert Solution & Answer
Chapter 2.4, Problem 1P
Explanation of Solution
Determining the dependency of the given sets of
Consider the given sets of vectors,
A matrix A is formed as given below; whose rows are the above given vectors:
The Gauss-Jordan method is applied to find the dependency of the above given sets of vectors.
Exchange row 3 and row 1, then the following matrix is obtained,
Multiply row 1 with 0.5, then the following matrix is obtained,
Now, replace row 2 by (row 2 – row 1), then the following matrix is obtained,
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solutionStudents have asked these similar questions
6. (Linear independence ): Is it true that if x; y; and z are linearly independent vectors overGF(q), then so are x + y; y + z, and z + x ?
Determine the value(s) of x so that the matrix below is the augmented matrixof an inconsistent linear system.
x
1
5
5
-3
2
Bu = ƒ and Cu = f might be solvable even though B and C are singular. Show that every vector f = Bu has ƒ1 + ƒ2+ ……. +fn = 0. Physical meaning: the external forces balance. Linear algebra meaning: Bu = ƒ is solvable when ƒ is perpendicular to the all – ones column vector e = (1, 1, 1, 1…) = ones (n, 1).
Chapter 2 Solutions
Introduction to mathematical programming
Ch. 2.1 - Prob. 1PCh. 2.1 - Prob. 2PCh. 2.1 - Prob. 3PCh. 2.1 - Prob. 4PCh. 2.1 - Prob. 5PCh. 2.1 - Prob. 6PCh. 2.1 - Prob. 7PCh. 2.2 - Prob. 1PCh. 2.3 - Prob. 1PCh. 2.3 - Prob. 2P
Ch. 2.3 - Prob. 3PCh. 2.3 - Prob. 4PCh. 2.3 - Prob. 5PCh. 2.3 - Prob. 6PCh. 2.3 - Prob. 7PCh. 2.3 - Prob. 8PCh. 2.3 - Prob. 9PCh. 2.4 - Prob. 1PCh. 2.4 - Prob. 2PCh. 2.4 - Prob. 3PCh. 2.4 - Prob. 4PCh. 2.4 - Prob. 5PCh. 2.4 - Prob. 6PCh. 2.4 - Prob. 7PCh. 2.4 - Prob. 8PCh. 2.4 - Prob. 9PCh. 2.5 - Prob. 1PCh. 2.5 - Prob. 2PCh. 2.5 - Prob. 3PCh. 2.5 - Prob. 4PCh. 2.5 - Prob. 5PCh. 2.5 - Prob. 6PCh. 2.5 - Prob. 7PCh. 2.5 - Prob. 8PCh. 2.5 - Prob. 9PCh. 2.5 - Prob. 10PCh. 2.5 - Prob. 11PCh. 2.6 - Prob. 1PCh. 2.6 - Prob. 2PCh. 2.6 - Prob. 3PCh. 2.6 - Prob. 4PCh. 2 - Prob. 1RPCh. 2 - Prob. 2RPCh. 2 - Prob. 3RPCh. 2 - Prob. 4RPCh. 2 - Prob. 5RPCh. 2 - Prob. 6RPCh. 2 - Prob. 7RPCh. 2 - Prob. 8RPCh. 2 - Prob. 9RPCh. 2 - Prob. 10RPCh. 2 - Prob. 11RPCh. 2 - Prob. 12RPCh. 2 - Prob. 13RPCh. 2 - Prob. 14RPCh. 2 - Prob. 15RPCh. 2 - Prob. 16RPCh. 2 - Prob. 17RPCh. 2 - Prob. 18RPCh. 2 - Prob. 19RPCh. 2 - Prob. 20RPCh. 2 - Prob. 21RPCh. 2 - Prob. 22RP
Knowledge Booster
Similar questions
Given vectors :u = (5,2) ; v = (-2,5) w = (0,3) ; q = (10,4)4.1 Calculate the following dot products:u.v ; (u.v).w ; u.(3w) , u.(w-v)4.2 Calculate‖?‖ ; d(u,v) ; ‖? − ?‖24.3 u and v are they orthogonal4.4 u and w are they orthogonal4.5 find c real number that satisfy q = c.u4.6 Deduce that q and u are parallel4.7 Normalize vector w
arrow_forward
Using the algorithm for Gauss-Jordan without pivoting, code a function myGaussJordanNoPivotthat takes as input a matrix A and collection of q right-hand sides bl forming the q columns of of a matrix B. Yourfunction shall return the q solution vectors xl as columns of a matrix X. Do not use any Matlab build-in functionsfor solving matrices
arrow_forward
USING PYTHON
A tridiagonal matrix is one where the only nonzero elements are the ones on the main diagonal (i.e., ai,j where j = i) and the ones immediately above and belowit(i.e.,ai,j wherej=i+1orj=i−1).
Write a function that solves a linear system whose coefficient matrix is tridiag- onal. In this case, Gauss elimination can be made much more efficient because most elements are already zero and don’t need to be modified or added.
Please show steps and explain.
arrow_forward
If there is a non-singular matrix P such as P-1AP=D, matrix A is called a diagonalizable matrix. A, n x n square matrix is diagonalizable if and only if matrix A has n linearly independent eigenvectors. In this case, the diagonal elements of the diagonal matrix D are the eigenvalues of the matrix A.
A=({{1, -1, -1}, {1, 3, 1}, {-3, 1, -1}}) :
1
-1
-1
1
3
1
-3
1
-1
a)Write a program that calculates the eigenvalues and eigenvectors of matrix A using NumPy.
b)Write the program that determines whether the D matrix is diagonal by calculating the D matrix, using NumPy.
#UsePython
arrow_forward
Please Help me With This Problem
Write a function to find the norm of a matrix. The norm is defined as the square root of the sum of squaresof all elements in the matrix
arrow_forward
Solve in C++ using the given code and avoid using vectors
arrow_forward
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 +…
arrow_forward
Minimize the following function using the Karnaugh Map. Show complete solution please
arrow_forward
If ? = [0 0 11 0 01 1 01 1 01 1 11 0 0101] is a parity check matrix for a linear code, then list all itscodewords.Hint: Solve the homogeneous system ? ∙ ??? = 0.
arrow_forward
If matrix A is a 2 x 3 matrix, it can be multiplie by matrix B to obtain AB only if matrix B has:A. 2 rowsB. 2 columnsC. 3 rowsD. 3 columns
arrow_forward
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.
arrow_forward
arrow_back_ios
arrow_forward_ios
Recommended textbooks for you
Operations Research : Applications and Algorithms
Computer Science
ISBN:9780534380588
Author:Wayne L. Winston
Publisher:Brooks Cole
Operations Research : Applications and Algorithms
Computer Science
ISBN:9780534380588
Author:Wayne L. Winston
Publisher:Brooks Cole