We study \textit{rescaled gradient dynamical systems} in a Hilbert space $\mathcal{H}$, where the implicit discretization in a finite-dimensional Euclidean space leads to high-order methods for solving monotone equations (MEs). Our framework generalizes the celebrated dual extrapolation method~\citep{Nesterov-2007-Dual} from first order to high order via appeal to the regularization toolbox of optimization theory~\citep{Nesterov-2021-Implementable, Nesterov-2021-Inexact}. We establish the existence and uniqueness of a global solution and analyze the convergence properties of solution trajectories. We also present discrete-time counterparts of our high-order continuous-time methods, and we show that the $p^{th}$-order method achieves an ergodic rate of $O(k^{-(p+1)/2})$ in terms of a restricted merit function and a pointwise rate of $O(k^{-p/2})$ in terms of a residue function. Under regularity conditions, the restarted version of $p^{th}$-order methods achieves local convergence with the order $p \geq 2$.
翻译:我们用Hilbert 空间 $\ mathcal{H} 来研究\ textit{ recasser 梯度动态系统} 。 在Hilbert 空间 $\ mathcal{H} 中, 隐含的离散性导致解决单色方程式( MEs) 的高度方法。 我们的框架将备受关注的双倍外推法 ⁇ citep{ Nesterep{ Nesterov-2021- 可执行的规范工具箱Nesterov-2021- Inact} 从第一顺序到高顺序。 我们建立全球解决方案的存在和独特性, 分析解决方案轨迹的趋同性。 我们还介绍了我们高端连续时间方法的离异对应方。 我们显示, $p} $- serford 方法在限定的功绩功能和美元( k ⁇ - p+1/2) 的点准速率方面, 在常规条件下, 以 $_\\ 重新组合法 下, 以 $xqrequestal sequestal res ral ration 。