Unfolding the Interdisciplinary Complexities of Climate Science: Fuxi-Climate Foundational Model
为解决气候科学研究中的跨学科复杂性问题，本文提出Fuxi-Climate基础模型，通过增强的结构化推理能力来改善对气候风险和过渡路径的分析。
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Distributed Stochastic Momentum Tracking with Local Updates: Achieving Optimal Communication and Iteration Complexities
To address the fundamental trade-off between high communication overhead and low convergence efficiency in distributed stochastic optimization, this paper proposes Local Momentum Tracking (LMT). LMT integrates multi-step local updates, momentum tracking, and loopless Chebyshev acceleration (LCA), enabling multiple local computations per communication round while preserving global coordination and computational efficiency. Theoretically, when the number of local updates is appropriately chosen, LMT simultaneously achieves optimal communication complexity O(1/ε) and iteration complexity O(1/ε), marking the first linear speedup under multi-step local update settings without requiring strong convexity or second-order smoothness assumptions. Empirical evaluations demonstrate that LMT significantly outperforms state-of-the-art methods in bandwidth-constrained networks, effectively breaking the communication–computation trade-off bottleneck.
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Unfolding the Interdisciplinary Complexities of Climate Science: Fuxi-Climate Foundational Model
为解决气候科学研究中的跨学科复杂性问题，本文提出Fuxi-Climate基础模型，通过增强的结构化推理能力来改善对气候风险和过渡路径的分析。
0 citationsRead paper
Distributed Stochastic Momentum Tracking with Local Updates: Achieving Optimal Communication and Iteration Complexities
To address the fundamental trade-off between high communication overhead and low convergence efficiency in distributed stochastic optimization, this paper proposes Local Momentum Tracking (LMT). LMT integrates multi-step local updates, momentum tracking, and loopless Chebyshev acceleration (LCA), enabling multiple local computations per communication round while preserving global coordination and computational efficiency. Theoretically, when the number of local updates is appropriately chosen, LMT simultaneously achieves optimal communication complexity O(1/ε) and iteration complexity O(1/ε), marking the first linear speedup under multi-step local update settings without requiring strong convexity or second-order smoothness assumptions. Empirical evaluations demonstrate that LMT significantly outperforms state-of-the-art methods in bandwidth-constrained networks, effectively breaking the communication–computation trade-off bottleneck.
0 citationsRead paper