Published Paper
Inserted: 16 mar 2018
Last Updated: 18 may 2020
Journal: ISSAC '18: Proceedings of the 2018 ACM International Symposium on Symbolic and Algebraic Computation
Year: 2018
Doi: https://dl.acm.org/doi/proceedings/10.1145/3208976
Abstract:
Let $A_0, \ldots, A_n$ be $m \times m$ symmetric matrices with entries in $\mathbb{Q}$, and let $A(x)$ be the linear pencil $A_0+x_1 A_1+\cdots+x_n A_n$, where $x=(x_1,\ldots,x_n)$ are unknowns. The linear matrix inequality (LMI) $A(x) \succeq 0$ defines the subset of $\mathbb{R}^n$, called spectrahedron, containing all points $x$ such that $A(x)$ has non-negative eigenvalues. The minimization of linear functions over spectrahedra is called semidefinite programming (SDP). Such problems appear frequently in control theory and real algebra, especially in the context of nonnegativity certificates for multivariate polynomials based on sums of squares. Numerical software for solving SDP are mostly based on the interior point method, assuming some non-degeneracy properties such as the existence of interior points in the admissible set. In this paper, we design an exact algorithm based on symbolic homotopy for solving semidefinite programs without assumptions on the feasible set, and we analyze its complexity. Because of the exactness of the output, it cannot compete with numerical routines in practice but we prove that solving such problems can be done in polynomial time if either $n$ or $m$ is fixed.