The LSQR iterative method is a Krylov subspace method for solving least-squares problems. Early termination is rare, and it is common for LSQR to require many iterations before an approximation of the solution with desired accuracy has been determined. In this paper, we present a restarted LSQR method and we use a new technique for accelerating the convergence of restated by adding some approximate error vectors to the Krylov subspace. The effectiveness of the new method is illustrated by several examples.

中文翻译：

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LSQR Krylov方法中的增强子空间

LSQR迭代方法是用于解决最小二乘问题的Krylov子空间方法。提前终止的情况很少见，对于LSQR，通常需要多次迭代才能确定具有所需精度的解的近似值。在本文中，我们提出了一种重启的LSQR方法，并通过向Krylov子空间中添加一些近似误差向量，使用了一种新技术来加速重整的收敛。几个示例说明了该新方法的有效性。