Adam's adaptive preconditioning and first-moment averaging improve high-probability tracking error in noise-dominated nonstationary regimes but can increase it under strong drift, where SGD achieves a smaller floor, with explicit beta-dependent bounds.
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Develops an ageing-aware nonlinear economic MPC for multi-carrier residential energy systems using physics-based battery models, reporting 10% grid cost reduction and 20% less degradation with LFP vs NMC cells plus 10%/5% gains over state-of-the-art in summer conditions.
A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.
citing papers explorer
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Adapt or Forget: Provable Tradeoffs Between Adam and SGD in Nonstationary Optimization
Adam's adaptive preconditioning and first-moment averaging improve high-probability tracking error in noise-dominated nonstationary regimes but can increase it under strong drift, where SGD achieves a smaller floor, with explicit beta-dependent bounds.
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Ageing-aware Energy Management for Residential Multi-Carrier Energy Systems
Develops an ageing-aware nonlinear economic MPC for multi-carrier residential energy systems using physics-based battery models, reporting 10% grid cost reduction and 20% less degradation with LFP vs NMC cells plus 10%/5% gains over state-of-the-art in summer conditions.
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Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers
A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.