Matrix powering, and more generally iterated matrix multiplication, is a fundamental linear algebraic primitive with myriad applications in computer science. Of particular interest is the problem’s space complexity as it constitutes the main route towards resolving the BPL vs. L problem. The seminal work by Saks and Zhou [JCSS ’99] gives a deterministic algorithm for approximating the product of n stochastic matrices of dimension w × w in space O(log3/2n + √logn · logw). The first improvement upon Saks–Zhou was achieved by Hoza [RANDOM ’21] who gave a logarithmic improvement in the n=poly(w) regime, attaining O(1/√loglogn · log3/2n) space.

Weighted pseudorandom generators (WPRGs), introduced by Braverman, Cohen and Garg [Braverman et al., 2020], are a generalization of pseudorandom generators (PRGs) in which arbitrary real weights are considered, rather than a probability mass. Braverman et al. constructed WPRGs against read once branching programs (ROBPs) with near-optimal dependence on the error parameter. Chattopadhyay and Liao [Eshan Chattopadhyay and Jyun-Jie Liao, 2020] somewhat simplified the technically involved BCG construction, also obtaining some improvement in parameters. In this work we devise an error reduction procedure for PRGs against ROBPs. More precisely, our procedure transforms any PRG against length n width w ROBP with error 1/poly(n) having seed length s to a WPRG with seed length s + O(logw/(ε) ⋅ log log1/(ε)). By instantiating our procedure with Nisan’s PRG [Noam Nisan, 1992] we obtain a WPRG with seed length O(log{n} ⋅ log(nw) + logw/(ε) ⋅ log log 1/(ε)). This improves upon [Braverman et al., 2020] and is incomparable with [Eshan Chattopadhyay and Jyun-Jie Liao, 2020]. Our construction is significantly simpler on the technical side and is conceptually cleaner. Another advantage of our construction is its low space complexity O(log{nw})+poly(log log1/(ε)) which is logarithmic in n for interesting values of the error parameter ε. Previous constructions (like [Braverman et al., 2020; Eshan Chattopadhyay and Jyun-Jie Liao, 2020]) specify the seed length but not the space complexity, though it is plausible they can also achieve such (or close) space complexity.

We strengthen the results from a recent work by the second author, achieving bounds on the weight distribution of binary linear codes that are successful under block-MAP (as well as bit-MAP) decoding on the BEC. We conclude that a linear code that is successful on the BEC can also decode over a range of binary memoryless symmetric (BMS) channels. In particular, applying the result of Kudekar, Kumar, Mondelli, Pfister, Şaşoğlu and Urbanke from STOC 2016, we prove that a Reed–Muller code of positive rate R decodes errors on the p with high probability if p < 1/2 − √2−R(1−2−R).

This work proves new results on the ability of binary Reed-Muller codes to decode from random errors and erasures. Specifically, we prove that RM codes with m variables and degree γm, for some explicit constant γ achieve capacity for random erasures (i.e. for the binary erasure channel) and for random errors (for the binary symmetric channel). Earlier, it was known that RM codes achieve capacity for the binary symmetric channel for degrees r = o(m). For the binary erasure channel it was known that RM codes achieve capacity for degree . Thus, our results provide a new range of parameters for which RM achieve capacity for these two well studied channels. In addition, our results imply that for every ϵ > 0 (in fact we can get up to RM codes of degree r < (1/2 – ϵ)m can correct a fraction of 1 – o(1) random erasures with high probability. We also show that, information theoretically, such codes can handle a fraction of random errors with high probability. For example, given noisy evaluations of a degree 0.499m polynomial, it is possible to interpolate it even if a random 0. 499 fraction of the evaluations were corrupted, with high probability. While the o(1) terms are not the correct ones to ensure capacity, these results show that RM codes of rates up to 1/poly(log n) (where n = 2m is the block length) are is some sense as good as capacity achieving codes. We obtain these results by proving improved bounds on the weight distribution of Reed-Muller codes of high degrees. Namely, given weight β ϵ (0, 1) we prove an upper bound on the number of codewords of relative weight at most β. We obtain new results in two different settings: for weights β < 1/2 and for weights that are close to 1/2. Our results for weights close to 1/2 also answer an open problem posed by Beame et al. [10].