Take each row and convert it into a column. The first row becomes the first column, the second row, the second column, etc.
It will either be a 1*23 row matrix or a 23*1 column matrix.
A cell in a matrix (or 2-dimensional array).
show that SQUARE MATRIX THE LINEAR DEPENDENCE OF THE ROW VECTOR?
#include<iostream> #include<iomanip> #include<vector> class matrix { private: // a vector of vectors std::vector< std::vector< int >> m_vect; public: // default constructor matrix(unsigned rows, unsigned columns) { // rows and columns must be non-zero if (!rows !columns) { throw; } m_vect.resize (rows); for (unsigned row=0; row<rows; ++row) { m_vect[row].resize (columns); for (unsigned column=0; column<columns; ++column) { m_vect[row][column] = 0; } } } // copy constructor matrix(const matrix& copy) { m_vect.resize (copy.rows()); for (unsigned row=0; row<copy.rows(); ++row) { m_vect[row] = copy.m_vect[row]; } } // assignment operator (uses copy/swap paradigm) matrix operator= (const matrix copy) { // note that copy was passed by value and was therefore copy-constructed // so no need to test for self-references (which should be rare anyway) m_vect.clear(); m_vect.resize (copy.rows()); for (unsigned row=0; row<copy.m_vect.size(); ++row) m_vect[row] = copy.m_vect[row]; } // allows vector to be used just as you would a 2D array (const and non-const versions) const std::vector< int >& operator[] (unsigned row) const { return m_vect[row]; } std::vector< int >& operator[] (unsigned row) { return m_vect[row]; } // product operator overload matrix operator* (const matrix& rhs) const; // read-only accessors to return dimensions const unsigned rows() const { return m_vect.size(); } const unsigned columns() const { return m_vect[0].size(); } }; // implementation of product operator overload matrix matrix::operator* (const matrix& rhs) const { // ensure columns and rows match if (columns() != rhs.rows()) { throw; } // instantiate matrix of required size matrix product (rows(), rhs.columns()); // calculate elements using dot product for (unsigned x=0; x<product.rows(); ++x) { for (unsigned y=0; y<product.columns(); ++y) { for (unsigned z=0; z<columns(); ++z) { product[x][y] += (*this)[x][z] * rhs[z][y]; } } } return product; } // output stream insertion operator overload std::ostream& operator<< (std::ostream& os, matrix& mx) { for (unsigned row=0; row<mx.rows(); ++row) { for (unsigned column=0; column<mx.columns(); ++column) { os << std::setw (10) << mx[row][column]; } os << std::endl; } return os; } int main() { matrix A(2,3); matrix B(3,4); int value=0, row, column; // initialise matrix A (incremental values) for (row=0; row<A.rows(); ++row) { for (column=0; column<A.columns(); ++column) { A[row][column] = ++value; } } std::cout << "Matrix A:\n\n" << A << std::endl; // initialise matrix B (incremental values) for (row=0; row<B.rows(); ++row) { for (column=0; column<B.columns(); ++column) { B[row][column] = ++value; } } std::cout << "Matrix B:\n\n" << B << std::endl; // calculate product of matrices matrix product = A * B; std::cout << "Product (A x B):\n\n" << product << std::endl; }
The minor is the determinant of the matrix constructed by removing the row and column of a particular element. Thus, the minor of a34 is the determinant of the matrix which has all the same rows and columns, except for the 3rd row and 4th column.
A matrix with a row or a column of zeros cannot have an inverse.Proof:Let A denote a matrix which has an entire row or column of zeros. If B is any matrix, then AB has an entire rows of zeros, or BA has an entire column of zeros. Thus, neither AB nor BA can be the identity matrix, so A cannot have an inverse, or A cannot be invertible.Since A is not invertible, then Ax = b has not a unique solution.
Matrix Add/* Program MAT_ADD.C**** Illustrates how to add two 3X3 matrices.**** Peter H. Anderson, Feb 21, '97*/#include <stdio.h>void add_matrices(int a[][3], int b[][3], int result[][3]);void print_matrix(int a[][3]);void main(void){int p[3][3] = { {1, 3, -4}, {1, 1, -2}, {-1, -2, 5} };int q[3][3] = { {8, 3, 0}, {3, 10, 2}, {0, 2, 6} };int r[3][3];add_matrices(p, q, r);printf("\nMatrix 1:\n");print_matrix(p);printf("\nMatrix 2:\n");print_matrix(q);printf("\nResult:\n");print_matrix(r);}void add_matrices(int a[][3], int b[][3], int result[][3]){int i, j;for(i=0; i<3; i++){for(j=0; j<3; j++){result[i][j] = a[i][j] + b[i][j];}}}void print_matrix(int a[][3]){int i, j;for (i=0; i<3; i++){for (j=0; j<3; j++){printf("%d\t", a[i][j]);}printf("\n");}}
It is either a row vector (1 x m matrix) or a column vector (n x 1 matrix).
It is apace provided for a set of data all occupying the same column in the spreadsheet's matrix so that they can be referenced as a set using the column header or individually by the column/row intersections.
Since the columns of AT equal the rows of A by definition, they also span the same space, so yes, they are equivalent.
An incidence matrix is a mathematical matrix showing a relationship between two different types of objects. The first class can be written as X and the second as Y with one row for each element of X and one column for each element of Y. The entry in row x and column 1 should be 1 if x and y are related to one another. The entry in row x and column 1 should be 0 if they are not related to each other,