Smoothing the gap between NP and ER
Not on the map. Why?
Abstract (arXiv)
We study algorithmic problems that belong to the complexity class of the existential theory of the reals (ER). A problem is ER-complete if it is as hard as the problem ETR and if it can be written as an ETR formula. Traditionally, these problems are studied in the real RAM, a model of computation that assumes that the storage and comparison of real-valued numbers can be done in constant space and time, with infinite precision. The complexity class ER is often called a real RAM analogue of NP, since the problem ETR can be viewed as the real-valued variant of SAT. In this paper we prove a real RAM analogue to the Cook-Levin theorem which shows that ER membership is equivalent to having a verification algorithm that runs in polynomial-time on a real RAM. This gives an easy proof of ER-membership, as verification algorithms on a real RAM are much more versatile than ETR-formulas. We use this result to construct a framework to study ER-complete problems under smoothed analysis. We show that for a wide class of ER-complete problems, its witness can be represented with logarithmic input-precision by using smoothed analysis on its real RAM verification algorithm. This shows in a formal way that the boundary between NP and ER (formed by inputs whose solution witness needs high input-precision) consists of contrived input. We apply our framework to well-studied ER-complete recognition problems which have the exponential bit phenomenon such as the recognition of realizable order types or the Steinitz problem in fixed dimension.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Dec 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 4; 2022: 2; 2023: 0; 2024: 1; 2025: 2; 2026: 1.
10 papers.
- 2022Training Fully Connected Neural Networks is existsℝ-Completecited by 15
- 2021ReLU Neural Networks of Polynomial Size for Exact Maximum Flow Computationcited by 12
- 2022List-Decodable Covariance Estimationcited by 8
- 2021On Classifying Continuous Constraint Satisfaction Problemscited by 5
- 2021Covering Polygons is Even Hardercited by 2
- 2024On Probabilistic and Causal Reasoning with Summation Operatorscited by 2
- 2025The Limits of Tractable Marginalizationcited by 2
- 2025Probabilistic and Causal Satisfiability: Constraining the Modelcited by 1
- 2021Training Neural Networks is existsmathbb R-completecited by 0
- 2026Approaching I/O-optimality for Approximate Attentioncited by 0
- 2026Approaching I/O-optimality for Approximate Attention22 May 2026
- 2025Probabilistic and Causal Satisfiability: Constraining the Model28 Apr 2025
- 2025The Limits of Tractable Marginalization17 Apr 2025
- 2024On Probabilistic and Causal Reasoning with Summation Operators5 May 2024
- 2022List-Decodable Covariance Estimation22 Jun 2022
- 2022Training Fully Connected Neural Networks is existsℝ-Complete4 Apr 2022
- 2021On Classifying Continuous Constraint Satisfaction Problems4 Jun 2021
- 2021Covering Polygons is Even Harder4 Jun 2021
- 2021Training Neural Networks is existsmathbb R-complete19 Feb 2021
- 2021ReLU Neural Networks of Polynomial Size for Exact Maximum Flow Computation12 Feb 2021