Solving systems of polynomial equations is one of the oldest and most important problems in computational mathematics and has many applications in several domains of science and engineering. It is an intrinsically hard problem with complexity at least single exponential in the number of variables. However, in most of the cases, the polynomial systems coming from applications have some kind of structure. For example, several problems in computer-aided design, robotics, computer vision, molecular biology and kinematics involve polynomial systems that are sparse that is, only a few monomials have non-zero coefficients.
We focus on exploiting the sparsity of the Newton polytopes of the polynomials to solve the systems faster than the worst case estimates. In this talk, I will present a Gröbner basis approach to solve sparse 0-dimensional systems whose input polynomials have different Newton polytopes. Under regularity assumptions, we can have an explicit combinatorial bound for the complexity of the algorithm.
This is joint work with Jean-Charles Faugère and Elias Tsigaridas.