Writing systems of linear equations in matrix form Ax=b, solving them using x=A−1b when
A system of linear equations can be written in matrix form Ax=b. For a system of 3 equations with 3 unknowns, we let x=⎝⎛xyz⎠⎞, and then A is a matrix whose rows are the equations of the system, and columns the coefficients of each variable. The vector b represents the constants on the RHS of the equations
can be written as
Once a system of equations is written in the form Ax=b, so long as A−1 exists we can find the solution by left-multiplying both sides by the inverse:
This solution is unique.
When detA=0, A−1 does not exist. As such, a unique solution does not exist. Instead, the system has either no solutions, or it has infinitely many.
For systems of 2 or 3 equations, this can be interpreted as the lines / planes being parallel, and either lying on top of each other or having no intersections.
Nice work completing Systems of equations with Matrices, here's a quick recap of what we covered:
Exercises checked off
Writing systems of linear equations in matrix form Ax=b, solving them using x=A−1b when
A system of linear equations can be written in matrix form Ax=b. For a system of 3 equations with 3 unknowns, we let x=⎝⎛xyz⎠⎞, and then A is a matrix whose rows are the equations of the system, and columns the coefficients of each variable. The vector b represents the constants on the RHS of the equations
can be written as
Once a system of equations is written in the form Ax=b, so long as A−1 exists we can find the solution by left-multiplying both sides by the inverse:
This solution is unique.
When detA=0, A−1 does not exist. As such, a unique solution does not exist. Instead, the system has either no solutions, or it has infinitely many.
For systems of 2 or 3 equations, this can be interpreted as the lines / planes being parallel, and either lying on top of each other or having no intersections.
Nice work completing Systems of equations with Matrices, here's a quick recap of what we covered:
Exercises checked off