Abstract: We present a general framework for derandomizing random linear codes with respect to a broad class of permutation-invariant properties, known as local properties, which encompass several standard notions such as distance, list-decoding, list-recovery, and perfect hashing. Our approach extends the classical Alon–Edmonds–Luby (AEL) construction through a modified formalism of local coordinate-wise linear (LCL) properties, introduced by Levi, Mosheiff, and Shagrithaya (2025). The main theorem demonstrates that if random linear codes satisfy the complement of an LCL property P with high probability, then one can construct explicit codes satisfying the complement of P as well, with an enlarged yet constant alphabet size. This gives the first explicit constructions for list recovery, as well as special cases (e.g., list recovery with erasures, zero-error list recovery, perfect hash matrices), with parameters matching those of random linear codes. More broadly, our constructions realize the full range of parameters associated with these properties at the same level of optimality as in the random setting, thereby offering a systematic pathway from probabilistic guarantees to explicit codes that attain them. Furthermore, our derandomization of random linear codes also admits efficient (list) decoding via recently developed expander-based decoders.
Joint work with Fernando Granha Jeronimo.
Bio: Nikhil Shagrithaya is a final-year Ph.D. student at the University of Michigan, Ann Arbor, advised by Prof. Mahdi Cheraghchi. He completed his masters at IIT Kanpur and his undergrad at the LNM Institute of Information Technology. His research interests lie in coding theory, pseudorandomness, and computational complexity theory.