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版本更新 · v4arXiv:2604.18216

EFX Need Not Exist

Hannaneh Akrami, Simon Mackenzie, Alexander Mayorov, Kurt Mehlhorn, Shreyas Srinivas, Mashbat Suzuki, Christoph Weidenbach

原文摘要

The existence of envy-free up to any good (EFX) allocations has long been a central open problem in discrete fair division. We resolve it in the negative by constructing a counterexample with three agents and eight goods. The instance is invariant under every simultaneous permutation of the agents and their associated good types, and this symmetry yields a short, self-contained proof of nonexistence. The counterexample can be realized with weighted-coverage, and hence submodular, valuations, and it extends to $n$ agents and $m$ goods for all $n\ge3$ and $m\ge n+5$. For three agents, eight goods is the minimum: every instance with three agents and at most seven goods admits an EFX allocation under arbitrary monotone valuations. The proof relies on a new analysis, valid for any number of goods, of when the invariant-based technique of [AACGMM25] produces an EFX allocation. This analysis strictly extends the class of MMS-feasible valuations. We also give explicit bounds for approximate EFX. For subadditive valuations, $α$-EFX allocations need not exist for any $α>2^{-1/6}\approx0.891$, and for submodular valuations for any $α>0.958$. On the positive side, if two of three agents have arbitrary monotone valuations and the third has a subadditive valuation, there is always an allocation that is EFX for the first two and $2^{-1/3}$-EFX ($\approx0.794$) for the third. A first version of the counterexample and of the seven-goods result was obtained using SAT/SMT solving, and the insights it provided guided our classical, pen-and-paper proofs. We also describe the encoding, which we believe is a valuable tool in its own right: to our knowledge, this is the first use of SAT/SMT solving for EFX.

cs.GT · cs.DS

版本更新 · v6arXiv:2508.05597

确定性通信复杂度的NP困难性与加性不可近似性

NP-Hardness and Additive Inapproximability of Deterministic Communication Complexity

Serge Gaspers, Tao Zixu He, Simon Mackenzie

AI 速读 基于摘要

作者报告:给定布尔函数完整真值表,确定两方计算该函数所需最小最坏情况通信比特数是NP困难的,且在任何固定加性误差下不可近似。

cs.CC · cs.DS · math.CO

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Given the full truth table of a Boolean function whose input is split between two players, we study the complexity of determining the minimum worst-case number of bits they must communicate to compute it. This is a basic meta-complexity question, posed by Yao in 1979. We prove NP-hardness of approximation within every fixed additive error for deterministic communication complexity, under deterministic polynomial-time reductions. The result holds both in the standard model and in Yao's original model, where the players alternate after every bit and only the receiver of the last bit must determine the answer. In particular, it resolves the exact computation question under Yao's original conventions. Earlier versions of this same paper established exact NP-hardness for the standard model, independently and concurrently with Hirahara, Ilango, and Loff. In both models, a polynomial-time estimator with any fixed additive error would yield a polynomial-time $3$-SAT algorithm, deterministic or bounded-error randomized according to the estimator. For the standard model, we also give quantitative implications for errors that grow with the number $T$ of table entries. A bounded-error randomized polynomial-time estimator with additive error $c\log T/\log\log T$, for any fixed $0\le c<1/8$, yields a bounded-error randomized $3$-SAT algorithm on $N$ variables and $O(N)$ clauses running in time $2^{O(N^α)}$ for every fixed $8c<α<1$. A deterministic polynomial-time estimator with additive error $c_*\log\log T$, for an effective absolute constant $c_*>0$, yields a deterministic algorithm running in time $2^{O((\log N)^2)}$ on such formulas.