Homotopy techniques for solving sparse column support determinantal polynomial systems
George Labahn (SCG), Mohab Safey El Din (PolSys), \'Eric Schost (SCG),, Thi Xuan Vu (PolSys, SCG)

TL;DR
This paper introduces a novel randomized homotopy algorithm that efficiently computes isolated solutions of sparse polynomial systems with determinantal structure, relevant in optimization and geometry, with complexity bounds considering sparsity and degree constraints.
Contribution
It presents the first algorithm exploiting both determinantal structure and sparsity for solving such polynomial systems.
Findings
Algorithm is polynomial in the number of solutions and structural parameters.
Provides complexity bounds for systems with weighted degree constraints.
Applicable to critical point computations invariant under symmetric group actions.
Abstract
Let be a field of characteristic zero with its algebraic closure. Given a sequence of polynomials and a polynomial matrix , with , we are interested in determining the isolated points of , the algebraic set of points in at which all polynomials in and all -minors of vanish, under the assumption . Such polynomial systems arise in a variety of applications including for example polynomial optimization and computational geometry. We design a randomized sparse homotopy algorithm for computing the isolated points in which takes advantage of the determinantal structure of the system…
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