Underdetermined system

In mathematics, a system of linear equations or a system of polynomial equations is considered underdetermined if there are fewer equations than unknowns (in contrast to an overdetermined system, where there are more equations than unknowns). The terminology can be explained using the concept of constraint counting. Each unknown can be seen as an available degree of freedom. Each equation introduced into the system can be viewed as a constraint that restricts one degree of freedom. Therefore, the critical case (between overdetermined and underdetermined) occurs when the number of equations and the number of free variables are equal. For every variable giving a degree of freedom, there exists a corresponding constraint removing a degree of freedom. The underdetermined case, by contrast, occurs when the system has been underconstrained—that is, when the unknowns outnumber the equations. An underdetermined linear system has either no solution or infinitely many solutions. For example, is an underdetermined system without any solution; any system of equations having no solution is said to be inconsistent. On the other hand, the system is consistent and has an infinitude of solutions, such as (x, y, z) = (1, −2, 2), (2, −3, 2), and (3, −4, 2). All of these solutions can be characterized by first subtracting the first equation from the second, to show that all solutions obey z = 2; using this in either equation shows that any value of y is possible, with x = −1 − y. More specifically, according to the Rouché–Capelli theorem, any system of linear equations (underdetermined or otherwise) is inconsistent if the rank of the augmented matrix is greater than the rank of the coefficient matrix. If, on the other hand, the ranks of these two matrices are equal, the system must have at least one solution; since in an underdetermined system this rank is necessarily less than the number of unknowns, there are indeed an infinitude of solutions, with the general solution having k free parameters where k is the difference between the number of variables and the rank.