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1. Performance of several Lanczos eigensolvers with HISQ fermions

   https://doi.org/10.22323/1.396.0053  

   Jeong, H; DeTar, C.; Gottlieb, S. (January 2022, Pos proceedings of science)

   We investigate the state-of-the-art Lanczos eigensolvers available in the Grid and QUDA libraries. They include Implicitly Restarted Lanczos, Thick-Restart Lanczos, and Block Lanczos. We measure and analyze their performance for the Highly Improved Staggered Quark (HISQ) Dirac operator. We also discuss optimization of Chebyshev acceleration. 

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2. Near-Optimal Approximation of Matrix Functions by the Lanczos Method

   Amsel, Noah; Chen, Tyler; Greenbaum, Anne; Musco, Cameron; Musco, Christopher (December 2024, Advances in Neural Information Processing Systems)

   Approximating the action of a matrix function $$f(\vec{A})$$ on a vector $$\vec{b}$$ is an increasingly important primitive in machine learning, data science, and statistics, with applications such as sampling high dimensional Gaussians, Gaussian process regression and Bayesian inference, principle component analysis, and approximating Hessian spectral densities. Over the past decade, a number of algorithms enjoying strong theoretical guarantees have been proposed for this task. Many of the most successful belong to a family of algorithms called \emph{Krylov subspace methods}. Remarkably, a classic Krylov subspace method, called the Lanczos method for matrix functions (Lanczos-FA), frequently outperforms newer methods in practice. Our main result is a theoretical justification for this finding: we show that, for a natural class of \emph{rational functions}, Lanczos-FA matches the error of the best possible Krylov subspace method up to a multiplicative approximation factor. The approximation factor depends on the degree of $f(x)$'s denominator and the condition number of $$\vec{A}$$, but not on the number of iterations $$k$$. Our result provides a strong justification for the excellent performance of Lanczos-FA, especially on functions that are well approximated by rationals, such as the matrix square root. 

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3. Syotti: scalable bait design for DNA enrichment

   https://doi.org/10.1093/bioinformatics/btac226  

   Alanko, Jarno N; Slizovskiy, Ilya B; Lokshtanov, Daniel; Gagie, Travis; Noyes, Noelle R; Boucher, Christina (June 2022, Bioinformatics)

   Abstract Motivation Bait enrichment is a protocol that is becoming increasingly ubiquitous as it has been shown to successfully amplify regions of interest in metagenomic samples. In this method, a set of synthetic probes (‘baits’) are designed, manufactured and applied to fragmented metagenomic DNA. The probes bind to the fragmented DNA and any unbound DNA is rinsed away, leaving the bound fragments to be amplified for sequencing. Metsky et al. demonstrated that bait-enrichment is capable of detecting a large number of human viral pathogens within metagenomic samples. Results We formalize the problem of designing baits by defining the Minimum Bait Cover problem, show that the problem is NP-hard even under very restrictive assumptions, and design an efficient heuristic that takes advantage of succinct data structures. We refer to our method as Syotti. The running time of Syotti shows linear scaling in practice, running at least an order of magnitude faster than state-of-the-art methods, including the method of Metsky et al. At the same time, our method produces bait sets that are smaller than the ones produced by the competing methods, while also leaving fewer positions uncovered. Lastly, we show that Syotti requires only 25 min to design baits for a dataset comprised of 3 billion nucleotides from 1000 related bacterial substrains, whereas the method of Metsky et al. shows clearly super-linear running time and fails to process even a subset of 17% of the data in 72 h. Availability and implementation https://github.com/jnalanko/syotti. Supplementary information Supplementary data are available at Bioinformatics online. 

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4. The Computational Advantage of MIP^∗ Vanishes in the Presence of Noise

   https://doi.org/10.4230/LIPIcs.CCC.2024.30  

   Dong, Yangjing; Fu, Honghao; Natarajan, Anand; Qin, Minglong; Xu, Haochen; Yao, Penghui (January 2024, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)

   Santhanam, Rahul (Ed.)

   The class MIP^* of quantum multiprover interactive proof systems with entanglement is much more powerful than its classical counterpart MIP [Babai et al., 1991; Zhengfeng Ji et al., 2020; Zhengfeng Ji et al., 2020]: while MIP = NEXP, the quantum class MIP^* is equal to RE, a class including the halting problem. This is because the provers in MIP^* can share unbounded quantum entanglement. However, recent works [Qin and Yao, 2021; Qin and Yao, 2023] have shown that this advantage is significantly reduced if the provers' shared state contains noise. This paper attempts to exactly characterize the effect of noise on the computational power of quantum multiprover interactive proof systems. We investigate the quantum two-prover one-round interactive system MIP^*[poly,O(1)], where the verifier sends polynomially many bits to the provers and the provers send back constantly many bits. We show noise completely destroys the computational advantage given by shared entanglement in this model. Specifically, we show that if the provers are allowed to share arbitrarily many EPR states, where each EPR state is affected by an arbitrarily small constant amount of noise, the resulting complexity class is equivalent to NEXP = MIP. This improves significantly on the previous best-known bound of NEEEXP (nondeterministic triply exponential time) [Qin and Yao, 2021]. We also show that this collapse in power is due to the noise, rather than the O(1) answer size, by showing that allowing for noiseless EPR states gives the class the full power of RE = MIP^*[poly, poly]. Along the way, we develop two technical tools of independent interest. First, we give a new, deterministic tester for the positivity of an exponentially large matrix, provided it has a low-degree Fourier decomposition in terms of Pauli matrices. Secondly, we develop a new invariance principle for smooth matrix functions having bounded third-order Fréchet derivatives or which are Lipschitz continuous. 

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5. Faster Spectral Density Estimation and Sparsification in the Nuclear Norm

   Jin, Yujia; Karmarkar, Ishani; Musco, Christopher; Sidford, Aaron; Singh, Apoorv Vikram (June 2024, Proceedings of Machine Learning Research)

   We consider the problem of estimating the spectral density of the normalized adjacency matrix of an $$n$$-node undirected graph. We provide a randomized algorithm that, with $$O(n\epsilon^{-2})$$ queries to a degree and neighbor oracle and in $$O(n\epsilon^{-3})$$ time, estimates the spectrum up to $$\epsilon$$ accuracy in the Wasserstein-1 metric. This improves on previous state-of-the-art methods, including an $$O(n\epsilon^{-7})$$ time algorithm from [Braverman et al., STOC 2022] and, for sufficiently small $$\epsilon$$, a $$2^{O(\epsilon^{-1})}$$ time method from [Cohen-Steiner et al., KDD 2018]. To achieve this result, we introduce a new notion of graph sparsification, which we call \emph{nuclear sparsification}. We provide an $$O(n\epsilon^{-2})$$-query and $$O(n\epsilon^{-2})$$-time algorithm for computing $$O(n\epsilon^{-2})$$-sparse nuclear sparsifiers. We show that this bound is optimal in both its sparsity and query complexity, and we separate our results from the related notion of additive spectral sparsification. Of independent interest, we show that our sparsification method also yields the first \emph{deterministic} algorithm for spectral density estimation that scales linearly with $$n$$ (sublinear in the representation size of the graph). 

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