Here is a simple way to see it that will help you both understand and remember. Take two equations in two unknowns. You can generalize later. Make a 2x2 matrix using the coefficients only. Now if you multiply this equation by the vector (x,Y) written as a column and placed on the right side of the matrix and you have the 2 equations you started with. Now put the constants, that is to say what each equation is equal to, on the right side of the = sign. If you invert the coefficient matrix on the left, the 2x2 one, and multiply both sides by that inverse, the equation is solved. There is another method known as Cramer's rule that can help you to solve equations using matrices. I suggest you look that one up if you are interested or ask for some more help!
When the matrix of coefficients is singular.
This is the case when there is only one set of values for each of the variables that satisfies the system of linear equations. It requires the matrix of coefficients. A to be invertible. If the system of equations is y = Ax then the unique solution is x = A-1y.
A system of linear equations is two or more simultaneous linear equations. In mathematics, a system of linear equations (or linear system) is a collection of linear equations involving the same set of variables.
The answer depends on the nature of the equations. For a system of linear equations, the [generalised] inverse matrix is probably simplest. For a mix of linear and non-linear equations the options include substitution, graphic methods, iteration and numerical approximations. The latter includes trail and improvement. Then there are multi-dimensional versions of "steepest descent".
An independent system of linear equations is a set of vectors in Rm, where any other vector in Rm can be written as a linear combination of all of the vectors in the set. The vector equation and the matrix equation can only have the trivial solution (x=0).
When the matrix of coefficients is singular.
That they, along with the equations, are invisible!
An "inconsistent" set of equations. If they are all linear equations then the matrix of coefficients is singular.
Rank of a matrix is used to find consistency of linear system of equations.As we know most of the engineering problems land up with the problem of finding solution of a linear system of equations ,at that point rank of matrix is useful.
This is the case when there is only one set of values for each of the variables that satisfies the system of linear equations. It requires the matrix of coefficients. A to be invertible. If the system of equations is y = Ax then the unique solution is x = A-1y.
If one (or more) of the equations can be expressed as a linear combination of the others. This is equivalent to the statements the matrix of coefficients does not have an inverse (or is singular), or the determinant of the matrix of coefficients is zero.
A system of linear equations is two or more simultaneous linear equations. In mathematics, a system of linear equations (or linear system) is a collection of linear equations involving the same set of variables.
The answer depends on the nature of the equations. For a system of linear equations, the [generalised] inverse matrix is probably simplest. For a mix of linear and non-linear equations the options include substitution, graphic methods, iteration and numerical approximations. The latter includes trail and improvement. Then there are multi-dimensional versions of "steepest descent".
An independent system of linear equations is a set of vectors in Rm, where any other vector in Rm can be written as a linear combination of all of the vectors in the set. The vector equation and the matrix equation can only have the trivial solution (x=0).
In simple terms all that it means that there are more solutions than you can count!If the equations are all linear, some possibilities are given below (some are equivalent statements):there are fewer equations than variablesthe matrix of coefficients is singularthe matrix of coefficients cannot be invertedone of the equations is a linear combination of the others
A = coefficient matrix (n x n) B = constant matrix (n x 1)
One equation is simply a multiple of the other. Equivalently, the equations are linearly dependent; or the matrix of coefficients is singular.