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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Spectral Refutations of Semirandom $k$-LIN over Larger Fi...
Nicholas Kocurek, Peter Manohar · 2025-08-26 · via cs.DS updates on arXiv.org

We study the problem of strongly refuting semirandom $k$-LIN$(\mathbb{F})$ instances: systems of $k$-sparse inhomogeneous linear equations over a finite field $\mathbb{F}$. For the case of $\mathbb{F} = \mathbb{F}_2$, this is the well-studied problem of refuting semirandom instances of $k$-XOR, where the works of [GKM22,HKM23] establish a tight trade-off between runtime and clause density for refutation: for any choice of a parameter $\ell$, they give an $n^{O(\ell)}$-time algorithm to certify that there is no assignment that can satisfy more than $\frac{1}{2} + \varepsilon$-fraction of constraints in a semirandom $k$-XOR instance, provided that the instance has $O(n) \cdot \left(\frac{n}{\ell}\right)^{k/2 - 1} \log n /\varepsilon^4$ constraints, and the work of [KMOW17] provides good evidence that this tight up to a $\mathrm{polylog}(n)$ factor via lower bounds for the Sum-of-Squares hierarchy. However for larger fields, the only known results for this problem are established via black-box reductions to the case of $\mathbb{F}_2$, resulting in an $|{\mathbb{F}}|^{3k}$ gap between the current best upper and lower bounds. In this paper, we give an algorithm for refuting semirandom $k$-LIN$(\mathbb{F})$ instances with the "correct" dependence on the field size $|{\mathbb{F}}|$. For any choice of a parameter $\ell$, our algorithm runs in $(|{\mathbb{F}}|n)^{O(\ell)}$-time and strongly refutes semirandom $k$-LIN$(\mathbb{F})$ instances with at least $O(n) \cdot \left(\frac{|{\mathbb{F}^*}| n}{\ell}\right)^{k/2 - 1} \log(n |{\mathbb{F}^*}|) /\varepsilon^4$ constraints. We give good evidence that this dependence on the field size $|{\mathbb{F}}|$ is optimal by proving a lower bound for the Sum-of-Squares hierarchy that matches this threshold up to a $\mathrm{polylog}(n |{\mathbb{F}^*}|)$ factor. Our results also extend to the more general case of finite Abelian groups.