We study a preconditioner for a Hermitian positive definite linear system, which is obtained as the solution of a matrix nearness problem based on the Bregman log determinant divergence. The preconditioner is on the form of a Hermitian positive definite matrix plus a low-rank matrix. For this choice of structure, the generalised eigenvalues of the preconditioned system are easily calculated, and we show that the preconditioner is optimal in the sense that it minimises the $\ell_2$ condition number of the preconditioned matrix. We develop practical numerical approximations of the preconditioner based on the randomised singular value decomposition (SVD) and the Nystr\"om approximation and provide corresponding approximation results. Furthermore, we prove that the Nystr\"om approximation is in fact also a matrix approximation in a range-restricted Bregman divergence and establish several connections between this divergence and matrix nearness problems in different measures. Numerical examples are provided to support the theoretical results.

翻译：本文研究了一种适用于埃尔米特正定线性系统的预条件子，该预条件子通过基于Bregman对数行列式散度的矩阵逼近问题求解得到。预条件子采用“埃尔米特正定矩阵加低秩矩阵”的结构形式。对于这种结构选择，预条件系统的广义特征值易于计算，我们证明了该预条件子在最小化预条件矩阵的$\ell_2$条件数意义上具有最优性。我们基于随机奇异值分解（SVD）和Nyström近似方法开发了实用的数值逼近方案，并给出了相应的逼近结果。进一步地，我们证明了Nyström近似实际上也是一种在受限值域Bregman散度下的矩阵逼近，并建立了该散度与不同度量下矩阵逼近问题之间的若干联系。数值算例验证了理论结果。