## Section: New Results

### Multilinear Polynomial Systems: Root Isolation and Bit Complexity

In , we exploit structure in polynomial system solving by considering polyno-mials that are linear in subsets of the variables. We focus on algorithms and their Boolean complexity for computing isolating hyperboxes for all the isolated complex roots of well-constrained, unmixed systems of multilinear polynomials based on resultant methods. We enumerate all expressions of the multihomogeneous (or multigraded) resultant of such systems as a determinant of Sylvester-like matrices, aka generalized Sylvester matrices. We construct these matrices by means of Weyman homological complexes, which generalize the Cayley-Koszul complex. The computation of the determinant of the resultant matrix is the bottleneck for the overall complexity. We exploit the quasi-Toeplitz structure to reduce the problem to efficient matrix-vector multiplication, which corresponds to multivariate polynomial multiplication, by extending the seminal work on Macaulay matrices of Canny, Kaltofen, and Yagati  to the multi-homogeneous case. We compute a rational univariate representation of the roots, based on the primitive element method. In the case of 0-dimensional systems we present a Monte Carlo algorithm with probability of success $1-1/{2}^{\eta }$, for a given $\eta \ge 1$, and bit complexity ${O}_{B}\left({n}^{2}{D}^{4+ϵ}\left({n}^{N+1}+\tau \right)+n{D}^{2+ϵ}\eta \left(D+\eta \right)\right)$ for any $ϵ>0$, where n is the number of variables, $D$ equals the multilinear Bézout bound, $N$ is the number of variable subsets, and $\tau$ is the maximum coefficient bitsize. We present an algorithmic variant to compute the isolated roots of overdetermined and positive-dimensional systems. Thus our algorithms and complexity analysis apply in general with no assumptions on the input.