Application to Elimination Theory Introduction Many problems in linear algebra (and many other branches of science) boil down to solving a system of linear equations in a number of variables. This in turn means finding common solutions to some “polynomial” equations of degree 1 (hyperplanes). In many cases, we are faced with “nonlinear” system of polynomial equations in more than one variable. Geometrically, this means finding common points to some “surfaces”. Like the Gaussian elimination for linear systems, the elimination theory in general is about eliminating a number of unknowns from a system of polynomial equations in one or more variables to get an easier equivalent system.

One way of find common solutions to polynomial equations is to solve each equation separately and then compare all the solutions. As you can guess, this is not an efficient way especially if the goal is only to show the existence of a solution.

To understand the importance of elimination theory, let us start by considering the following “simple” example.

Example Consider a system of two quadratic equations in one variable x: We would like to find a necessary and sufficient condition for the existence of a common solution for the system If f(x) and g(x) have a common solution, then they must have a common linear factor, say, L. Let Then both q1(x) and q2(x) must be linear, and we can write (the signs chosen in q2(x) will make more sense in a moment)  for some constants A1, B1, A2 and B2. Now, since then Explicitly, Expanding and collecting terms in the above equation gives: This equation is only possible if the coefficients of x, x2, x3 and the constant term are all equal to 0. This gives the following homogeneous system with variables arranged in this order: A2, B2, A1, B1: In order for this system to have a nontrivial solution, its coefficient matrix must be noninvertible. In other words, its determinant must be zero: which is equivalent to since the determinant of a matrix is equal to the determinant of its transpose. This determinant is called the (Sylvester) resultant of f(x) and g(x). Note that the resultant in this case is the determinant of a 4x4 matrix consisting of the coefficients of the two polynomials together with 0’s arranged in a very special way.

Here is the formal definition of the resultant:

Definition Let be two polynomials of degree m and n respectively such that am ≠ 0 or bn ≠ 0. If m ≤ n, we define the resultant of f(x) and g(x) to be the following determinant: Note that Res(f(x), g(x)) is the determinant of a square matrix of size m+n

Example If then As a generalization of the first example, we have the following:

Theorem Let be two polynomials of degree m and n respectively such that am ≠ 0 or bn ≠ 0. Then the system has a solution if and only if  Res(f(x), g(x))=0.

# Example 1Without solving the polynomial equations, show that the following system has solutions.

Solution We compute the resultant of the two polynomials  therefore, the polynomials f(x), g(x) have a common root by the above theorem.

One can use the above theorem to determine if a polynomial system in more than one variable has a solution. The trick is to look at the polynomials in the system as polynomials in one variable with coefficients polynomials in the other variables. The next example illustrates that idea.

Example 2 Solve the following system: # Solution We may look at this system as polynomials in y with coefficients polynomials in x: In order to have a common solution, one must have: This is equivalent to .

The problem is reduced then to solving a polynomial equation in one variable x. Although the solution of this equation does not look easy, one can use a numerical approach to estimate the solutions. These solutions correspond to the x-coordinates of the intersection points. Note also that the original system ca be written as  so any solution to the system is an intersection of an ellipse and a circle that can be found geometrically.