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Finding the minimum distance of linear codes is an NP-hard problem. Traditionally, this computation has been addressed by means of the design of algorithms that find, by a clever exhaustive search, a linear combination of some generating…

Information Theory · Computer Science 2020-11-02 M. P. Cuéllar , J. Gómez-Torrecillas , F. J. Lobillo , G. Navarro

Linear codes with a few weights have been widely investigated in recent years. In this paper, we mainly use Gauss sums to represent the Hamming weights of a class of $q$-ary linear codes under some certain conditions, where $q$ is a power…

Information Theory · Computer Science 2016-05-12 Ziling Heng , Qin Yue

While code large language models have demonstrated remarkable progress in code generation, the generated code often exhibits poor runtime efficiency, limiting its practical application in performance-sensitive scenarios. To address this…

Software Engineering · Computer Science 2025-08-29 Yunlong Feng , Yang Xu , Xiao Xu , Binyuan Hui , Junyang Lin

We continue the investigation of locally testable codes, i.e., error-correcting codes for whom membership of a given word in the code can be tested probabilistically by examining it in very few locations. We give two general results on…

Information Theory · Computer Science 2007-07-16 Eli Ben-Sasson , Madhu Sudan

Error correction codes are a crucial part of the physical communication layer, ensuring the reliable transfer of data over noisy channels. The design of optimal linear block codes capable of being efficiently decoded is of major concern,…

Information Theory · Computer Science 2024-05-08 Yoni Choukroun , Lior Wolf

A linear code of length $n$ over a finite chain ring $R$ with residue field $\F_q$ is a $R$-submodule of $R^n$. A $R$-linear code is a code over $\F_q$ (not necessarily linear) which is the generalized Gray map image of a linear code over…

Information Theory · Computer Science 2025-12-03 Cristina Fernández-Córdoba , Sergi Sánchez-Aragón , Mercè Villanueva

We generalize the leverage score sampling sketch for $\ell_2$-subspace embeddings, to accommodate sampling subsets of the transformed data, so that the sketching approach is appropriate for distributed settings. This is then used to derive…

Information Theory · Computer Science 2024-06-27 Neophytos Charalambides , Mert Pilanci , Alfred Hero

Dating back to the seminal work of von Neumann [von Neumann, Automata Studies, 1956], it is known that error correcting codes can overcome faulty circuit components to enable robust computation. Choosing an appropriate code is non-trivial…

Information Theory · Computer Science 2025-10-06 Anirudh Krishna , Gilles Zémor

First-order primal-dual methods are appealing for their low memory overhead, fast iterations, and effective parallelization. However, they are often slow at finding high accuracy solutions, which creates a barrier to their use in…

Optimization and Control · Mathematics 2023-12-05 David Applegate , Oliver Hinder , Haihao Lu , Miles Lubin

In this paper, we propose a linear representation of BCJR maximum a posteriori probability (MAP) decoding of a rate 1/2 convolutional code (CC), referred to as the linear MAP decoding (LMAP). We discover that the MAP forward and backward…

Information Theory · Computer Science 2025-04-22 Yonghui Li , Chentao Yue , Branka Vucetic

The sum-rank metric is the mixture of the Hamming and rank metrics. The sum-rank metric found its application in network coding, locally repairable codes, space-time coding, and quantum-resistant cryptography. Linearized Reed-Solomon (LRS)…

Information Theory · Computer Science 2026-02-10 Kuo Shang , Chen Yuan , Ruiqi Zhu

In this work, we present a new version of non-binary VT codes that are capable of correcting a single deletion or single insertion. Moreover, we provide the first known linear time algorithms that encode user messages into these codes of…

Information Theory · Computer Science 2022-12-22 Tuan Thanh Nguyen , Kui Cai , Paul H. Siegel

We construct a new family of explicit codes that are list decodable to capacity and achieve an optimal list size of $O(\frac{1}{\epsilon})$. In contrast to existing explicit constructions of codes achieving list decoding capacity, our…

Information Theory · Computer Science 2025-02-12 Fernando Granha Jeronimo , Tushant Mittal , Shashank Srivastava , Madhur Tulsiani

Graph encoder embedding, a recent technique for graph data, offers speed and scalability in producing vertex-level representations from binary graphs. In this paper, we extend the applicability of this method to a general graph model, which…

Machine Learning · Statistics 2024-10-24 Cencheng Shen

This study investigates the problem of learning linear block codes optimized for Belief-Propagation decoders significantly improving performance compared to the state-of-the-art. Our previous research is extended with an enhanced system…

Signal Processing · Electrical Eng. & Systems 2025-10-02 Louis-Adrien Dufrène , Quentin Lampin , Guillaume Larue

In distributed storage systems, locally repairable codes (LRCs) are designed to reduce disk I/O and repair costs by enabling recovery of each code symbol from a small number of other symbols. To handle multiple node failures,…

Information Theory · Computer Science 2023-07-11 Jing Qiu , Fang-Wei Fu

In this work, lossy distributed compression of pairs of correlated sources is considered. Conventionally, Shannon's random coding arguments -- using randomly generated unstructured codebooks whose blocklength is taken to be asymptotically…

Information Theory · Computer Science 2020-10-21 Farhad Shirani , S. Sandeep Pradhan

Secure codes are widely-studied combinatorial structures which were introduced for traitor tracing in broadcast encryption. To determine the maximum size of such structures is the main research objective. In this paper, we investigate the…

Information Theory · Computer Science 2021-08-24 Bingchen Qian , Xin Wang , Gennian Ge

Generalized Linear Models (GLM) form a wide class of regression and classification models, where prediction is a function of a linear combination of the input variables. For statistical inference in high dimension, sparsity inducing…

Machine Learning · Statistics 2022-08-25 Mathurin Massias , Samuel Vaiter , Alexandre Gramfort , Joseph Salmon

Weak superimposed codes are combinatorial structures related closely to generalized cover-free families, superimposed codes, and disjunct matrices in that they are only required to satisfy similar but less stringent conditions. This class…

Information Theory · Computer Science 2024-09-17 Yu Tsunoda , Yuichiro Fujiwara