Regularity in hypergraphs is fine-grained equivalent to the general case for clique detection, enabling a complete classification of k-sparse Boolean CSP optimization complexity by constraint degree: linear for d≤1, clique-equivalent for d=2, and exhaustive-search for d≥3 under 3-uniform hyperclique
35 Dmitriy Zhuk
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
Sparsity helps for k-independent set only below certain density thresholds, with new algorithms achieving O(min(n^{ωk/3} + m^{k/3}, n^k)) time and conditional lower bounds showing brute-force necessity above thresholds for many binary constraint families.
Local syndrome-based preprocessing accelerates BP decoders for quantum LDPC codes, delivering up to 10x speedup on the [[144,12,12]] code while maintaining or improving logical error rates.
citing papers explorer
-
The Role of Regularity in (Hyper-)Clique Detection and Implications for Optimizing Boolean CSPs
Regularity in hypergraphs is fine-grained equivalent to the general case for clique detection, enabling a complete classification of k-sparse Boolean CSP optimization complexity by constraint degree: linear for d≤1, clique-equivalent for d=2, and exhaustive-search for d≥3 under 3-uniform hyperclique
-
When Does Sparsity Help for k-Independent Set in Hypergraphs and Other Boolean CSPs?
Sparsity helps for k-independent set only below certain density thresholds, with new algorithms achieving O(min(n^{ωk/3} + m^{k/3}, n^k)) time and conditional lower bounds showing brute-force necessity above thresholds for many binary constraint families.
-
Accelerating BP-based decoders for QLDPC Codes with Local Syndrome-Based Preprocessing
Local syndrome-based preprocessing accelerates BP decoders for quantum LDPC codes, delivering up to 10x speedup on the [[144,12,12]] code while maintaining or improving logical error rates.