We present a mathematical and numerical investigation to the shrinkingdimer saddle dynamics for finding any-index saddle points in the solution landscape. Due to the dimer approximation of Hessian in saddle dynamics, the local Lipschitz assumptions and the strong nonlinearity for the saddle dynamics, it remains challenges for delicate analysis, such as the the boundedness of the solutions and the dimer error. We address these issues to bound the solutions under proper relaxation parameters, based on which we prove the error estimates for numerical discretization to the shrinking-dimer saddle dynamics by matching the dimer length and the time step size. Furthermore, the Richardson extrapolation is employed to obtain a high-order approximation. The inherent reason of requiring the matching of the dimer length and the time step size lies in that the former serves a different mesh size from the later, and thus the proposed numerical method is close to a fully-discrete numerical scheme of some spacetime PDE model with the Hessian in the saddle dynamics and its dimer approximation serving as a "spatial operator" and its discretization, respectively, which in turn indicates the PDE nature of the saddle dynamics.
翻译:我们用数学和数字来调查缩小的马鞍动态,以寻找解决方案景观中的任何指数性马鞍点。由于赫森在马鞍动态中的偏差近似值、当地Lipschitz假设和马鞍动态的强非线性,这仍然是微妙分析的挑战,例如解决方案的界限和稀释错误。我们解决这些问题是为了在适当的放松参数下将解决方案捆绑起来,根据这些参数,我们通过匹配马鞍动态中的稀释长度和时间步骤大小,来证明数字分解到缩小的马鞍动态的误差估计值。此外,理查德森外推法被用于获得一个高顺序的近似值。要求将马鞍长度和时间级大小匹配的内在原因在于前者的网状尺寸与后者的不同,因此拟议的数字方法接近于某些空间时PDE模型的完全分解的数值计划,而赫森在马鞍动态中的模型和其稀释近近近近,分别作为“空间操作者”及其离析,这反过来表明马鞍的特性。