Current Organizations / Institutes

The organizations/institutes from the scholar's latest article

Previous Organizations / Institutes

The organizations / institutes from the scholar's other articles in the past ten years


Bin Shi has a total of 21 co-authors. Here are the top co-authors by number of shared publications:


Latest Publications

  • Open Access

    Numerical Construction of Quasi-Periodic Solutions Beyond Symplectic Integrators

    by Mingwei Fu, Bin Shi

    Symplectic integrators are the established standard for long-term simulations of nearly-integrable Hamiltonian systems due to their preservation of geometric structures. However, they suffer from an inherent limitation: secular phase-shift errors. While the qualitative ''shape'' of invariant tori is preserved, the numerical solution gradually drifts along the torus, leading to a phase-lag accumulation that degrades long-term positional accuracy. Inspired by the Craig-Wayne-Bourgain (CWB) scheme, originally developed as an analytical tool for infinite-dimensional systems, we introduce a numerical operator that incorporates frequency updates into a dimension-enlarged Newton iteration to compute quasi-periodic solutions. Unlike conventional time-stepping integrators, our alternating numerical procedure eliminates phase-lag accumulation by directly solving for instantaneous positions and phase angles. Theoretically, provided sufficient computational resources, the phase error can be reduced arbitrarily, remaining independent of the total integration time. Our algorithm translates the Nash-Moser iteration into a practical numerical framework, marking a significant departure from traditional Kolmogorov-Arnold-Moser (KAM) theory. While KAM provides rigorous existence proofs, its requirement for global Diophantine conditions and the total exclusion of resonant sets render it numerically inaccessible. By employing a ''step-by-step'' exclusion process and incrementally enlarging the dimension, our algorithm resolves irrationality conditions locally. This approach demonstrates that the ''numerical irrationality problem'' is not an intrinsic barrier to computation, offering a constructive, executable alternative to the non-executable nature of global KAM-based methods.

  • Open Access

    On Pseudospectral Concentration for Rank-1 Sampling

    by Kuo Gai, Bin Shi

    Pseudospectral analysis serves as a powerful tool in matrix computation and the study of both linear and nonlinear dynamical systems. Among various numerical strategies, random sampling, especially in the form of rank-$1$ perturbations, offers a practical and computationally efficient approach. Moreover, due to invariance under unitary similarity, any complex matrix can be reduced to its upper triangular form, thereby simplifying the analysis. In this study. we develop a quantitative concentration theory for the pseudospectra of complex matrices under rank-$1$ random sampling perturbations, establishing a rigorous probabilistic framework for spectral characterization. First, for normal matrices, we derive a regular concentration inequality and demonstrate that the separation radius scales with the dimension as $δ_d \sim 1/\sqrt{d}$. Next, for the equivalence class of nilpotent Jordan blocks, we exploit classical probabilistic tools, specifically, the Hanson-Wright concentration inequality and the Carbery-Wright anti-concentration inequality, to obtain singular concentration bounds, and demonstrate that the separation radius exhibits the same dimension-dependent scaling. This yields a singular pseudospectral concentration framework. Finally, observing that upper triangular Toeplitz matrices can be represented via the symbolic polynomials of nilpotent Jordan blocks, we employ partial fraction decomposition of rational functions to extend the singular framework to the equivalence class of upper triangular Toeplitz matrices.

  • Open Access

    A Family of Controllable Momentum Coefficients for Forward-Backward Accelerated Algorithms

    by Mingwei Fu, Bin Shi

    Nesterov's accelerated gradient method (NAG) marks a pivotal advancement in gradient-based optimization, achieving faster convergence compared to the vanilla gradient descent method for convex functions. However, its algorithmic complexity when applied to strongly convex functions remains unknown, as noted in the comprehensive review by Chambolle and Pock [2016]. This issue, aside from the critical step size, was addressed by Li et al. [2024b], with the monotonic case further explored by Fu and Shi [2024]. In this paper, we introduce a family of controllable momentum coefficients for forward-backward accelerated methods, focusing on the critical step size $s=1/L$. Unlike traditional linear forms, the proposed momentum coefficients follow an $α$-th power structure, where the parameter $r$ is adaptively tuned to $α$. Using a Lyapunov function specifically designed for $α$, we establish a controllable $O\left(1/k^{2α} \right)$ convergence rate for the NAG-$α$ method, provided that $r > 2α$. At the critical step size, NAG-$α$ achieves an inverse polynomial convergence rate of arbitrary degree by adjusting $r$ according to $α> 0$. We further simplify the Lyapunov function by expressing it in terms of the iterative sequences $x_k$ and $y_k$, eliminating the need for phase-space representations. This simplification enables us to extend the controllable $O \left(1/k^{2α} \right)$ rate to the monotonic variant, M-NAG-$α$, thereby enhancing optimization efficiency. Finally, by leveraging the fundamental inequality for composite functions, we extended the controllable $O\left(1/k^{2α} \right)$ rate to proximal algorithms, including the fast iterative shrinkage-thresholding algorithm (FISTA-$α$) and its monotonic counterpart (M-FISTA-$α$).

  • Open Access

    Comment on egusphere-2024-2747

    by Bin Shi, Dehai Luo, Wenqi Zhang

    In this paper, we explore optimal disturbances of blockings in the equivalent barotropic atmosphere using the conditional nonlinear optimal perturbation (CNOP) approach. Considering the initial blocking amplitude, the optimal disturbance exhibits a solitary wave-like pattern. As the size increases incrementally, the spatial pattern becomes more concentrated, and the nonlinear evolution becomes more pronounced. During the evolution, it only focuses on gradually intensifying the blocking amplitude without any other influence. Additionally, based on the medium-range experiments, the time-delay optimal disturbance appears to lead to larger errors, making it more challenging to predict. Considering the preexisting synoptic-scale eddies, the optimal disturbance displays a sharply concentrated pattern, even more concentrated by increasing the size. However, it is worth noting that the nonlinear evolution undergoes significant changes, compared to disturbances of the initial blocking amplitude. Meanwhile, we find that the optimal disturbance not only strongly impacts the amplitude of blockings but also their shape, making eddy straining and wave breaking more chaotic and predominant, further influencing the development of weather extremes. This suggests that blockings are more sensitive to perturbations of preexisting synoptic-scale eddies than initial blocking amplitudes. Furthermore, the perturbations of the synoptic-scale eddies are more likely to lead to the development of weather extremes, making them less predictable. In medium-range experiments, it is also found that time-delay disturbances result in larger errors, particularly during the decay period. Finally, we discuss how the variations of westerly wind influence optimal disturbances in spatial patterns and nonlinear evolution as well as their relation to predictability.