Are Sketch-and-Precondition Least Squares Solvers Numerically Stable?

IF 1.5 2区 数学 Q2 MATHEMATICS, APPLIED SIAM Journal on Matrix Analysis and Applications Pub Date : 2024-04-24 DOI:10.1137/23m1551973
Maike Meier, Yuji Nakatsukasa, Alex Townsend, Marcus Webb
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Abstract

SIAM Journal on Matrix Analysis and Applications, Volume 45, Issue 2, Page 905-929, June 2024.
Abstract. Sketch-and-precondition techniques are efficient and popular for solving large least squares (LS) problems of the form [math] with [math] and [math]. This is where [math] is “sketched” to a smaller matrix [math] with [math] for some constant [math] before an iterative LS solver computes the solution to [math] with a right preconditioner [math], where [math] is constructed from [math]. Prominent sketch-and-precondition LS solvers are Blendenpik and LSRN. We show that the sketch-and-precondition technique in its most commonly used form is not numerically stable for ill-conditioned LS problems. For provable and practical backward stability and optimal residuals, we suggest using an unpreconditioned iterative LS solver on [math] with [math]. Provided the condition number of [math] is smaller than the reciprocal of the unit roundoff, we show that this modification ensures that the computed solution has a backward error comparable to the iterative LS solver applied to a well-conditioned matrix. Using smoothed analysis, we model floating-point rounding errors to argue that our modification is expected to compute a backward stable solution even for arbitrarily ill-conditioned LS problems. Additionally, we provide experimental evidence that using the sketch-and-solve solution as a starting vector in sketch-and-precondition algorithms (as suggested by Rokhlin and Tygert in 2008) should be highly preferred over the zero vector. The initialization often results in much more accurate solutions—albeit not always backward stable ones.
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草图-条件最小二乘法求解器数值稳定吗?
SIAM 矩阵分析与应用期刊》,第 45 卷,第 2 期,第 905-929 页,2024 年 6 月。 摘要。草绘与条件技术是解决[math]与[math]和[math]形式的大型最小二乘法(LS)问题的高效且流行的方法。在迭代 LS 求解器利用正确的先决条件器[math]计算[math]的解之前,先将[math]"草绘 "为一个较小的矩阵[math],并在[math]中加入某个常数[math],其中[math]由[math]构造而成。著名的草图-条件 LS 求解器有 Blendenpik 和 LSRN。我们的研究表明,对于条件不佳的 LS 问题,最常用的草图和前提条件技术在数值上并不稳定。为了获得可证明的实用后向稳定性和最优残差,我们建议在[math]与[math]的[math]上使用无条件迭代 LS 求解器。只要[math]的条件数小于单位舍入的倒数,我们就能证明这种修改能确保计算解的后向误差与应用于条件良好矩阵的迭代 LS 求解器相当。通过平滑分析,我们建立了浮点舍入误差模型,从而证明即使对于任意条件不佳的 LS 问题,我们的修改也能计算出稳定的后向解。此外,我们还提供了实验证据,证明在草图和条件算法中使用草图和求解解作为起始向量(如 Rokhlin 和 Tygert 在 2008 年提出的建议)应比使用零向量更受青睐。初始化通常能得到更精确的解,尽管并不总是后向稳定的解。
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来源期刊
CiteScore
2.90
自引率
6.70%
发文量
61
审稿时长
6-12 weeks
期刊介绍: The SIAM Journal on Matrix Analysis and Applications contains research articles in matrix analysis and its applications and papers of interest to the numerical linear algebra community. Applications include such areas as signal processing, systems and control theory, statistics, Markov chains, and mathematical biology. Also contains papers that are of a theoretical nature but have a possible impact on applications.
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