Fine-grained hardness of CVP(P) -- Everything that we can prove (and nothing else)
Divesh Aggarwal, Huck Bennett, Alexander Golovnev, Noah, Stephens-Davidowitz

TL;DR
This paper establishes tight fine-grained hardness results for the Closest Vector Problem in various $oldsymbol{ ext{l}_p}$ norms, showing no subexponential algorithms exist under SETH for most $p$, and explores structural limitations for $oldsymbol{ ext{CVP}_2}$.
Contribution
It provides the first explicit, tight hardness bounds for approximate and preprocessing variants of $ ext{CVP}_p$ for most $p$, and reveals inherent structural restrictions for $ ext{CVP}_2$.
Findings
No $2^{(1-oldsymbol{ ext{ε}})n}$-time algorithms for $ ext{CVP}_p$ with $p otin 2oldsymbol{ ext{Z}}$ under SETH.
Hardness of approximation within a constant factor for $ ext{CVP}_p$ assuming a gap version of SETH.
Structural rigidity of $ ext{CVP}_2$ solutions prevents expressing complex Boolean functions.
Abstract
We show a number of fine-grained hardness results for the Closest Vector Problem in the norm (), and its approximate and non-uniform variants. First, we show that cannot be solved in time for all and , assuming the Strong Exponential Time Hypothesis (SETH). Second, we extend this by showing that there is no -time algorithm for approximating to within a constant factor for such assuming a "gap" version of SETH, with an explicit relationship between , , and the arity of the underlying hard CSP. Third, we show the same hardness result for (exact) with preprocessing (assuming non-uniform SETH). For exact "plain" , the same hardness result was shown in [Bennett, Golovnev,…
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Taxonomy
TopicsComplexity and Algorithms in Graphs · Machine Learning and Algorithms · Optimization and Search Problems
