We introduce a multimodel approach to solve coupled cluster equations, employing a quasi-Newton algorithm for the ground state and an Olsen algorithm for the excited states. In these algorithms, both of which can be viewed as Newton algorithms, the Jacobian matrix of a lower level coupled cluster model is used in Newton equations associated with the target model. Improvements in convergence then imply savings for sufficiently large molecular systems, since the computational cost of macroiterations scales more steeply with system size than the cost of microiterations. The multimodel approach is suitable when there is a lower level Jacobian matrix that is much more accurate than the zeroth order approximation. Applying the approach to the CC3 equations, using the CCSD approximation of the Jacobian, we show that the time spent to determine the ground and valence excited states can be significantly reduced. We also find improved convergence for core excited states, indicating that similar savings will be obtained with an explicit implementation of the core-valence separated CCSD Jacobian transformation.

中文翻译：

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加速的多模型牛顿型算法，用于更快地收敛基态和激发态耦合的簇方程。

我们引入了一种求解耦合簇方程的多模型方法，对基态采用了拟牛顿算法，对激发态采用了奥尔森算法。在这些都可以看作是牛顿算法的算法中，较低级耦合簇模型的雅可比矩阵用于与目标模型相关的牛顿方程。收敛性的提高则意味着可以节省足够大的分子系统，因为宏观迭代的计算成本随系统规模的增长比微迭代的成本要陡得多。当存在比零阶近似精确得多的较低级别的雅可比矩阵时，多模型方法是适用的。使用雅可比行列式的CCSD近似将方法应用于CC3方程，我们表明，确定基态和价态激发态所花费的时间可以大大减少。我们还发现核心激发态的收敛性得到了改善，这表明通过显式实现核心价分开的CCSD Jacobian变换将获得类似的节省。