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We show a linear sized reduction from the Maximum Cut Problem (Max-Cut) with completeness $1 - \varepsilon$ and soundness $1 - \varepsilon^{1/2}$ to the $\gamma$-Approximate Closest Vector Problem under any finite $\ell_p$-norm including $p = 2$. This reduction implies two headline results: (i) We show that any sub-exponential time (classical or quantum) algorithm for the $o(\sqrt{\log n}^{\frac{1}{p}})$-Approximate Closest Vector Problem in any finite $\ell_p$-norm implies a faster than the state-of-the-art (by Arora, Barak, and Steurer [\textit{Journal of the ACM}, 2015]) sub-exponential time (classical or quantum) algorithm for Max-Cut. This fills the gap between the results by Bennett, Golovnev, and Stephens-Davidowitz [\textit{FOCS} 2017] which had an almost optimal runtime lower bound but a very small approximation factor and the results by Dinur, Kindler, Raz, and Safra [\textit{Combinatorica}, 2003] which had an almost optimal approximation factor but small runtime lower bound, albeit using a different underlying hard problem; (ii) in combination with the classical results of Aggarwal and Kumar [\textit{FOCS} 2023] and our quantization of those results, there are no fine-grained reductions from $k$-SAT to Max-Cut with one-sided error, nor are there non-adaptive fine-grained (classical or quantum) reductions with two-sided error, unless the polynomial hierarchy collapses (or unless $\mathrm{NP} \subseteq \mathrm{pr} \text{-} \mathrm{QSZK}$ in the quantum case). The second result poses a significant barrier against proving the fine-grained complexity of Max-Cut using the Strong Exponential Time Hypothesis (or the Quantum Strong Exponential Time Hypothesis)., Comment: 35 pages, 3 figures

We show conditional hardness of Approximate Nearest Neighbor Search (ANN) under the $\ell_\infty$ norm with two simple reductions. Our first reduction shows that hardness of a special case of the Shortest Vector Problem (SVP), which captures many provably hard instances of SVP, implies a lower bound for ANN with polynomial preprocessing time under the same norm. Combined with a recent quantitative hardness result on SVP under $\ell_\infty$ (Bennett et al., FOCS 2017), our reduction implies that finding a $(1+\varepsilon)$-approximate nearest neighbor under $\ell_\infty$ with polynomial preprocessing requires near-linear query time, unless the Strong Exponential Time Hypothesis (SETH) is false. This complements the results of Rubinstein (STOC 2018), who showed hardness of ANN under $\ell_1$, $\ell_2$, and edit distance. Further improving the approximation factor for hardness, we show that, assuming SETH, near-linear query time is required for any approximation factor less than $3$ under $\ell_\infty$. This shows a conditional separation between ANN under the $\ell_1/ \ell_2$ norm and the $\ell_\infty$ norm since there are sublinear time algorithms achieving better than $3$-approximation for the $\ell_1$ and $\ell_2$ norm. Lastly, we show that the approximation factor of $3$ is a barrier for any naive gadget reduction from the Orthogonal Vectors problem., Comment: 16 pages

In 2010, P\v{a}tra\c{s}cu proposed the following three-phase dynamic problem, as a candidate for proving polynomial lower bounds on the operational time of dynamic data structures: I: Preprocess a collection of sets $\vec{S} = S_1, \ldots , S_k \subseteq [n]$, where $k=\operatorname{poly}(n)$. II: A set $T\subseteq [n]$ is revealed, and the data structure updates its memory. III: An index $i \in [k]$ is revealed, and the data structure must determine if $S_i\cap T=^? \emptyset$. P\v{a}tra\c{s}cu conjectured that any data structure for the Multiphase problem must make $n^\epsilon$ cell-probes in either Phase II or III, and showed that this would imply similar unconditional lower bounds on many important dynamic data structure problems. Alas, there has been almost no progress on this conjecture in the past decade since its introduction. We show an $\tilde{\Omega}(\sqrt{n})$ cell-probe lower bound on the Multiphase problem for data structures with general (adaptive) updates, and queries with unbounded but "layered" adaptivity. This result captures all known set-intersection data structures and significantly strengthens previous Multiphase lower bounds, which only captured non-adaptive data structures. Our main technical result is a communication lower bound on a 4-party variant of P\v{a}tra\c{s}cu's Number-On-Forehead Multiphase game, using information complexity techniques. We also show that a lower bound on P\v{a}tra\c{s}cu's original NOF game would imply a polynomial ($n^{1+\epsilon}$) lower bound on the number of wires of any constant-depth circuit with arbitrary gates computing a random $\tilde{O}(n)\times n$ linear operator $x \mapsto Ax$, a long-standing open problem in circuit complexity. This suggests that the NOF conjecture is much stronger than its data structure counterpart., Comment: 26 pages, 4 figures

We study the optimization problem faced by a perfectly informed principal in a Bayesian game, who reveals information to the players about the state of nature to obtain a desirable equilibrium. This signaling problem is the natural design question motivated by uncertainty in games and has attracted much recent attention. We present new hardness results for signaling problems in (a) Bayesian two-player zero-sum games, and (b) Bayesian network routing games. For Bayesian zero-sum games, when the principal seeks to maximize the equilibrium utility of a player, we show that it is NP-hard to obtain an additive FPTAS. Our hardness proof exploits duality and the equivalence of separation and optimization in a novel way. Further, we rule out an additive PTAS assuming planted clique hardness, which states that no polynomial time algorithm can recover a planted clique from an Erd\H{o}s-R\'enyi random graph. Complementing these, we obtain a PTAS for a structured class of zero-sum games (where obtaining an FPTAS is still NP-hard) when the payoff matrices obey a Lipschitz condition. Previous results ruled out an FPTAS assuming planted-clique hardness, and a PTAS only for implicit games with quasi-polynomial-size strategy sets. For Bayesian network routing games, wherein the principal seeks to minimize the average latency of the Nash flow, we show that it is NP-hard to obtain a (multiplicative) $(4/3 - \epsilon)$-approximation, even for linear latency functions. This is the optimal inapproximability result for linear latencies, since we show that full revelation achieves a $(4/3)$-approximation for linear latencies.

We prove a near optimal round-communication tradeoff for the two-party quantum communication complexity of disjointness. For protocols with $r$ rounds, we prove a lower bound of $\tilde{\Omega}(n/r + r)$ on the communication required for computing disjointness of input size $n$, which is optimal up to logarithmic factors. The previous best lower bound was $\Omega(n/r^2 + r)$ due to Jain, Radhakrishnan and Sen [JRS03]. Along the way, we develop several tools for quantum information complexity, one of which is a lower bound for quantum information complexity in terms of the generalized discrepancy method. As a corollary, we get that the quantum communication complexity of any boolean function $f$ is at most $2^{O(QIC(f))}$, where $QIC(f)$ is the prior-free quantum information complexity of $f$ (with error $1/3$)., Comment: 41 pages

We show that, assuming the (deterministic) Exponential Time Hypothesis, distinguishing between a graph with an induced $k$-clique and a graph in which all k-subgraphs have density at most $1-\epsilon$, requires $n^{\tilde \Omega(log n)}$ time. Our result essentially matches the quasi-polynomial algorithms of Feige and Seltser [FS97] and Barman [Bar15] for this problem, and is the first one to rule out an additive PTAS for Densest $k$-Subgraph. We further strengthen this result by showing that our lower bound continues to hold when, in the soundness case, even subgraphs smaller by a near-polynomial factor ($k' = k 2^{-\tilde \Omega (log n)}$) are assumed to be at most ($1-\epsilon$)-dense. Our reduction is inspired by recent applications of the "birthday repetition" technique [AIM14,BKW15]. Our analysis relies on information theoretical machinery and is similar in spirit to analyzing a parallel repetition of two-prover games in which the provers may choose to answer some challenges multiple times, while completely ignoring other challenges.