AbstractWe develop a two-timing perturbation analysis to provide quantitative insights on the existence of temporal ratchets in an exemplary system of a particle moving in a tank of fluid in response to an external vibration of the tank. We consider two-mode vibrations with angular frequencies$$\omega $$ and$$\alpha \omega $$ , where$$\alpha $$ is a rational number. If$$\alpha $$ is a ratio of odd and even integers (e.g.,$$\tfrac{2}{1},\,\tfrac{3}{2},\,\tfrac{4}{3}$$ ), the system yields a net response: here, a nonzero time-average particle velocity. Our first-order perturbation solution predicts the existence of temporal ratchets for$$\alpha =2$$ . Furthermore, we demonstrate, for a reduced model, that the temporal ratcheting effect for$$\alpha =\tfrac{3}{2}$$ and$$\tfrac{4}{3}$$ appears at the third-order perturbation solution. More importantly, we find closed-form formulas for the magnitude and direction of the induced net velocities for these$$\alpha $$ values. On a broader scale, our methodology offers a new mathematical approach to study the complicated nature of temporal ratchets in physical systems. Graphic abstract
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This content will become publicly available on April 7, 2027
Fast finite-sum optimization via cyclically-sampled Hessian averaging methods
Abstract We consider minimizing finite-sum objective functions via Hessian-averaging based subsampled Newton methods. These methods allow for gradient inexactness and have fixed per-iteration Hessian approximation costs. The recent work (Na et al. 2023) demonstrated that Hessian averaging can be utilized to achieve fast$$\mathcal {O}\left( \sqrt{\tfrac{\log k}{k}}\right) $$ local superlinear convergence for strongly convex functions in high probability, while maintaining fixed per-iteration Hessian costs. These methods, however, require gradient exactness and strong convexity, which poses challenges for their practical implementation. To address this concern we consider Hessian-averaged methods that allow gradient inexactness via norm condition based adaptive-sampling strategies. Furthermore, to better control the error in the subsampled Hessian approximations, we utilize Hessian averaging with deterministic cyclic sampling techniques instead of random sampling, which leads to fast local superlinear convergence. We develop a comprehensive convergence theory, including global linear and sublinear convergence rates for strongly convex and nonconvex functions, respectively. Additionally, we establish an improved local superlinear convergence rate of$$\mathcal {O}\left( \tfrac{1}{k}\right) $$ . Our analysis introduces novel techniques that differ from previous probabilistic approaches. We investigate the performance of these methods on logistic regression problems, demonstrating significant improvements in convergence over similar Hessian-averaging methods that utilize stochastic sampling.
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- Award ID(s):
- 2324643
- PAR ID:
- 10704301
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- Mathematical Programming
- ISSN:
- 0025-5610
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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