Exploiting Opportunities in Pseudorandomness (NSF CCF-1763299)

2020
Doron, Dean, Jack Murtagh, Salil Vadhan, and David Zuckerman. Spectral sparsification via bounded-independence sampling. Electronic Colloquium on Computational Complexity (ECCC), TR20-026, 2020. Publisher's VersionAbstract

Version History:

v1, 26 Feb 2020: https://arxiv.org/abs/2002.11237

We give a deterministic, nearly logarithmic-space algorithm for mild spectral sparsification of undirected graphs. Given a weighted, undirected graph \(G\) on \(n\) vertices described by a binary string of length \(N\), an integer \(k \leq \log n \) and an error parameter \(\varepsilon > 0\), our algorithm runs in space \(\tilde{O}(k \log(N ^. w_{max}/w_{min}))\) where \(w_{max}\) and \(w_{min}\) are the maximum and minimum edge weights in \(G\), and produces a weighted graph \(H\) with \(\tilde{O}(n^{1+2/k} / \varepsilon^2)\)expected edges that spectrally approximates \(G\), in the sense of Spielmen and Teng [ST04], up to an error of \(\varepsilon\).

Our algorithm is based on a new bounded-independence analysis of Spielman and Srivastava's effective resistance based edge sampling algorithm [SS08] and uses results from recent work on space-bounded Laplacian solvers [MRSV17]. In particular, we demonstrate an inherent tradeoff (via upper and lower bounds) between the amount of (bounded) independence used in the edge sampling algorithm, denoted by \(k\) above, and the resulting sparsity that can be achieved.

ECCC 2020.pdf
2019
Ahmadinejad, AmirMahdi, Jonathan Kelner, Jack Murtagh, John Peebles, Aaron Sidford, and Salil Vadhan. “High-precision estimation of random walks in small space.” arXiv: 1912.04525 [cs.CC], 2019 (2019). ArXiv VersionAbstract
In this paper, we provide a deterministic \(\tilde{O}(\log N)\)-space algorithm for estimating the random walk probabilities on Eulerian directed graphs (and thus also undirected graphs) to within inverse polynomial additive error \((ϵ = 1/\mathrm{poly}(N)) \) where \(N\) is the length of the input. Previously, this problem was known to be solvable by a randomized algorithm using space \(O (\log N)\) (Aleliunas et al., FOCS '79) and by a deterministic algorithm using space \(O (\log^{3/2} N)\) (Saks and Zhou, FOCS '95 and JCSS '99), both of which held for arbitrary directed graphs but had not been improved even for undirected graphs. We also give improvements on the space complexity of both of these previous algorithms for non-Eulerian directed graphs when the error is negligible \((ϵ=1/N^{ω(1)})\), generalizing what Hoza and Zuckerman (FOCS '18) recently showed for the special case of distinguishing whether a random walk probability is 0 or greater than ϵ.
We achieve these results by giving new reductions between powering Eulerian random-walk matrices and inverting Eulerian Laplacian matrices, providing a new notion of spectral approximation for Eulerian graphs that is preserved under powering, and giving the first deterministic \(\tilde{O}(\log N)\)-space algorithm for inverting Eulerian Laplacian matrices. The latter algorithm builds on the work of Murtagh et al. (FOCS '17) that gave a deterministic \(\tilde{O}(\log N)\)-space algorithm for inverting undirected Laplacian matrices, and the work of Cohen et al. (FOCS '19) that gave a randomized \(\tilde{O} (N)\)-time algorithm for inverting Eulerian Laplacian matrices. A running theme throughout these contributions is an analysis of "cycle-lifted graphs," where we take a graph and "lift" it to a new graph whose adjacency matrix is the tensor product of the original adjacency matrix and a directed cycle (or variants of one).
ARXIV 2019.pdf
Murtagh, Jack, Omer Reingold, Aaron Sidford, and Salil Vadhan. “Deterministic approximation of random walks in small space.” In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019), Dimitris Achlioptas and László A. Végh (Eds.). Vol. 145. Cambridge, Massachusetts (MIT) : Leibniz International Proceedings in Informatics (LIPIcs), 2019. Publisher's VersionAbstract
Version History: v1, 15 Mar. 2019: https://arxiv.org/abs/1903.06361v1
v2 in ArXiv, 25 Nov. 2019: https://arxiv.org/abs/1903.06361v2
 
Publisher's Version (APPROX-RANDOM 2019), 20 Sep 2019: 
 

We give a deterministic, nearly logarithmic-space algorithm that given an undirected graph \(G\), a positive integer \(r\), and a set \(S\) of vertices, approximates the conductance of \(S\) in the \(r\)-step random walk on \(G\) to within a factor of \(1+ϵ\), where \(ϵ > 0\) is an arbitrarily small constant. More generally, our algorithm computes an \(ϵ\)-spectral approximation to the normalized Laplacian of the \(r\)-step walk. Our algorithm combines the derandomized square graph operation (Rozenman and Vadhan, 2005), which we recently used for solving Laplacian systems in nearly logarithmic space (Murtagh, Reingold, Sidford, and Vadhan, 2017), with ideas from (Cheng, Cheng, Liu, Peng, and Teng, 2015), which gave an algorithm that is time-efficient (while ours is space-efficient) and randomized (while ours is deterministic) for the case of even \(r\) (while ours works for all \(r\)). Along the way, we provide some new results that generalize technical machinery and yield improvements over previous work. First, we obtain a nearly linear-time randomized algorithm for computing a spectral approximation to the normalized Laplacian for odd \(r\). Second, we define and analyze a generalization of the derandomized square for irregular graphs and for sparsifying the product of two distinct graphs. As part of this generalization, we also give a strongly explicit construction of expander graphs of every size.

APPROX-RANDOM 2019 ArXiv2019.pdf ArXiv 2019 v2.pdf
Agrawal, Rohit, Yi-Hsiu Chen, Thibaut Horel, and Salil Vadhan. “Unifying computational entropies via Kullback-Leibler divergence.” In Advances in Cryptology: CRYPTO 2019, A. Boldyreva and D. Micciancio, (Eds), 11693:831-858. Springer Verlag, Lecture Notes in Computer Science, 2019. Publisher's VersionAbstract
Version History: 
arXiv, first posted Feb 2019, most recently updated Aug 2019: https://arxiv.org/abs/1902.11202
 
We introduce hardness in relative entropy, a new notion of hardness for search problems which on the one hand is satisfied by all one-way functions and on the other hand implies both next-block pseudoentropy and inaccessible entropy, two forms of computational entropy used in recent constructions of pseudorandom generators and statistically hiding commitment schemes, respectively. Thus, hardness in relative entropy unifies the latter two notions of computational entropy and sheds light on the apparent “duality” between them. Additionally, it yields a more modular and illuminating proof that one-way functions imply next-block inaccessible entropy, similar in structure to the proof that one-way functions imply next-block pseudoentropy (Vadhan and Zheng, STOC ‘12).
CRYPTO 2019.pdf ArXiv2019.pdf