The Orthogonal Vectors Conjecture and Non-Uniform Circuit Lower Bounds
Ryan Williams
Abstract
A line of work has shown how nontrivial uniform algorithms for analyzing circuits can be used to derive non-uniform circuit lower bounds. We show how the non-existence of nontrivial circuit-analysis algorithms can also imply non-uniform circuit lower bounds. Our connections yield new win-win circuit lower bounds, and suggest a potential approach to refuting the Orthogonal Vectors Conjecture in the-dimensional case, which would be sufficient for refuting the Strong Exponential Time Hypothesis (SETH). For example, we show that at least one of the following holds: • There is ansuch that for infinitely many, read-once 2-DNFs onvariables cannot be simulated by non-uniform-size depth-two exact threshold circuits. It is already a notorious open problem to prove that the classdoes not have polynomial-size depth-two exact threshold circuits, so such a lower bound would be a significant advance in low-depth circuit complexity. In fact, a stronger lower bound holds in this case: theDisjointness Matrix (well-studied in communication complexity) cannot be expressed by a linear combination ofstructured matrices that we call “equality matrices”. • For everyand every, Orthogonal Vectors onvectors indimensions can be solved inuniform deterministic time. This case would provide a strong refutation of the Orthogonal Vectors conjecture, and of SETH: for example, CNF-SAT onvariables andclauses could be solved intime. Moreover, this case would imply non-uniform circuit lower bounds for the class, against Valiant series-parallel circuits. Inspired by this connection, we give evidence from SAT/SMT solvers that the first item (in particular, the Disjointness lower bound) may be false in its full generality. In particular, we present a systematic approach to solving Orthogonal Vectors via constant-sized decompositions of the Disjointness Matrix, which already yields interesting new algorithms. For example, using a linear combination of 6 equality matrices that expressDisjointness, we derive antime andspace algorithm for Orthogonal Vectors onvectors indimensions. We show similar results for counting pairs of orthogonal vectors.
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Cited by top-tier papers2
- Kronecker Powers, Orthogonal Vectors, and the Asymptotic SpectrumJosh Alman, Baitian LiFOCS 2025 · 2 citations
- Online Orthogonal Vectors RevisitedKarthik Gajulapalli, Alexander Golovnev, Samuel King, Sidhant SaraogiSODA 2026
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