What is a singular matrix error?
A singular matrix is a matrix that cannot be inverted, or, equivalently, that has determinant zero. So better make sure your matrix is non-singular (i.e., has non-zero determinant), since numpy.linalg.solve requires non-singular matrices.
A square matrix that is not singular, i.e., one that has a matrix inverse. Nonsingular matrices are sometimes also called regular matrices. A square matrix is nonsingular iff its determinant is nonzero (Lipschutz 1991, p. 45).
- square matrix is not invertible if at least one row or column is zero.
- The homogenous system Ax = 0 always has the solution x = 0. It follows that any homogeneous system of equations is always consistent. Any non-zero solutions, if they exist, are called non-trivial solutions. These may or may not exist.
- Each shows two lines that make up a system of equations. If the graphs of the equations intersect, then there is one solution that is true for both equations. If the graphs of the equations do not intersect (for example, if they are parallel), then there are no solutions that are true for both equations.
Updated: 22nd September 2018