pith. sign in

Elimination Without Eliminating: Computing Complements of Real Hypersurfaces Using Pseudo-Witness Sets

1 Pith paper cite this work. Polarity classification is still indexing.

1 Pith paper citing it
abstract

Many hypersurfaces in algebraic geometry, such as discriminants, arise as the projection of another variety. The real complement of such a hypersurface partitions its ambient space into open regions. In this paper, we propose a new method for computing these regions. Existing methods for computing regions require the explicit equation of the hypersurface as input. However, computing this equation by elimination can be computationally demanding or even infeasible. Our approach instead derives from univariate interpolation by computing the intersection of the hypersurface with a line. Such an intersection can be done using so-called pseudo-witness sets without computing a defining equation for the hypersurface - we perform elimination without actually eliminating. We implement our approach in a forthcoming Julia package and demonstrate, on several examples, that the resulting algorithm accurately recovers all regions of the real complement of a hypersurface.

citation-role summary

background 1

citation-polarity summary

fields

math.OC 1

years

2026 1

verdicts

UNVERDICTED 1

roles

background 1

polarities

background 1

representative citing papers

Copositive Matrices with Ordered Off-Diagonal Entries

math.OC · 2026-05-15 · unverdicted · novelty 7.0

Copositive matrices with nondecreasing off-diagonal entries admit a PSD plus nonnegative decomposition, which implies exactness of a natural relaxation for separable quadratic optimization over the simplex.

citing papers explorer

Showing 1 of 1 citing paper.

  • Copositive Matrices with Ordered Off-Diagonal Entries math.OC · 2026-05-15 · unverdicted · none · ref 2 · internal anchor

    Copositive matrices with nondecreasing off-diagonal entries admit a PSD plus nonnegative decomposition, which implies exactness of a natural relaxation for separable quadratic optimization over the simplex.