arXiv AI

EFX Allocation In (Multi)Hypergraphs

arXiv:2608. 03171v1 Announce Type: cross Abstract: We study fair allocations of indivisible goods among agents with heterogeneous monotone valuations.

arXiv AI
Aug 28

Simultaneous Envy and Equitability Guarantees

The paper investigates the compatibility of envy-freeness and equitability in fair division, focusing on both indivisible goods and chores. It shows that the relaxed notions EF1+EQ1 may not exist even for normalized additive valuations, but provides an algorithm that finds an EF1+EQ1 allocation for up to seven agents with binary goods. For chores, the authors prove that a stronger EFX+EQX guarantee always exists, regardless of normalization, and they also explore cross-notion ex‑ante and ex‑post fairness guarantees.

By Hadi Hosseini, Shraddha Pathak, Lirong Xia, Chengkai Zhang
arXiv AI
Sep 17

Independence-System Realisations in Single-Source Unsplittable Flow

The paper introduces a path‑closed framework for realizing independence systems via zero‑cost choices of primary terminals in directed acyclic flow instances, extending the triangle mechanism to all finite loopless independence systems. It shows that such systems, including all finite simple graphs and hypergraph independence systems without singleton forbidden hyperedges, admit polynomial‑size realizations measured by the incidence size of minimal forbidden sets. The authors further specialize to odd cycles, deriving a rational family that yields a fractional cheap‑selection vector violating the odd‑cycle inequality and establishing an exact additive‑congestion threshold that approaches 1/2 for large cycles.

By Koyar Afrasyab
arXiv Machine Learning
Sep 17

Maximum Strong Independent Sets in Hypergraphs: Reductions, Bounds, and Greedy Certificates

The paper investigates the maximum strong independent set problem in finite hypergraphs, where the goal is to find the largest vertex set that intersects each hyperedge in at most one vertex. It introduces an incidence-structural toolkit, proving exact reductions for dominance, incidence twins, and weight‑1 blocks, and derives closed‑form and low‑weight upper bounds. The authors also present puncturing and covering certificates that refine these bounds and analyze a layered greedy clustering algorithm driven by block weights and residual incidence, providing feasibility, maximality, conditional optimality, and incidence‑local complexity guarantees.

By Yingquan (Cody), Wu, Jason Cong