Authors reframe gadget reductions for CSP non-redundancy using hypergraph projections and shrinking factors to obtain improved super-linear lower bounds for select predicates, with SAT solvers used to discover reductions automatically.
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Introduces strong sparsification for 1-in-3-SAT by merging variables, relying on a sub-quadratic vector-set bound derived from the Polynomial Freiman-Ruzsa Theorem, with an application to hypergraph coloring approximation.
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Super-linear Lower Bounds for CSP Non-Redundancy via Shrinking Instances
Authors reframe gadget reductions for CSP non-redundancy using hypergraph projections and shrinking factors to obtain improved super-linear lower bounds for select predicates, with SAT solvers used to discover reductions automatically.
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Strong Sparsification for 1-in-3-SAT via Polynomial Freiman-Ruzsa
Introduces strong sparsification for 1-in-3-SAT by merging variables, relying on a sub-quadratic vector-set bound derived from the Polynomial Freiman-Ruzsa Theorem, with an application to hypergraph coloring approximation.