Two Linear Programs are Better than One
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Abstract (arXiv)
The extension complexity operatornamexc(P) of a polytope P is the minimum number of facets of a polytope that projects onto it. Motivated by the complexity of neural networks, Hertrich and Loho introduced the virtual extension complexity operatornamevxc(P) of a polytope P, which is the minimum of operatornamexc(Q) + operatornamexc(R) among all polytopes Q and R such that P+Q=R. We show the following results pertaining to virtual extension complexity. First, we resolve the core open problem on the topic by showing the virtual extension complexity can be exponentially smaller than extension complexity. Second, we show there are matroid base polytopes with exponential virtual extension complexity by adapting a semi-algebraic counting argument by Hrubevs that extends the counting arguments of Rothvoss. Finally, we show that the maximum virtual extension complexity of a polygon with n vertices is Theta(√n). This resolves an analogue of a question on extension complexity of Fiorini, Rothvoss, and Tiwary in 2011 in the case of virtual extension complexity.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 24 references that are in the map. First posted on arXiv 11 Oct 2026.
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References · 4
- 2016Understanding Deep Neural Networks with Rectified Linear Unitscited by 257
- 2021ReLU Neural Networks of Polynomial Size for Exact Maximum Flow Computationcited by 12
- 2024Neural Networks and (Virtual) Extended Formulationscited by 9
- 2025Arithmetic Circuits and Neural Networks for Regular Matroidscited by 2
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