Pages that link to "Item:Q2662325"
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The following pages link to Robust hybrid zero-order optimization algorithms with acceleration via averaging in time (Q2662325):
Displaying 13 items.
- A new one-point residual-feedback oracle for black-box learning and control (Q2063773) (← links)
- Robustness and averaging properties of a large-amplitude, high-frequency extremum seeking control scheme (Q2063785) (← links)
- A hybrid dynamical systems perspective on reinforcement learning for cyber-physical systems: vistas, open problems, and challenges (Q2094057) (← links)
- High-performance optimal incentive-seeking in transactive control for traffic congestion (Q2095352) (← links)
- Online optimization of switched LTI systems using continuous-time and hybrid accelerated gradient flows (Q2097731) (← links)
- 100 years of extremum seeking: a survey (Q6119740) (← links)
- A novel extremum seeking control to enhance convergence and robustness in the presence of nonlinear dynamic sensors (Q6130949) (← links)
- A distributed extremum seeking based resource allocation algorithm over switching networks (Q6149931) (← links)
- Uniting Nesterov and heavy ball methods for uniform global asymptotic stability of the set of minimizers (Q6152511) (← links)
- Learning generalized Nash equilibria in monotone games: a hybrid adaptive extremum seeking control approach (Q6164038) (← links)
- Initialization-free Lie-bracket extremum seeking (Q6590437) (← links)
- Prescribed-time stability in switching systems with resets: a hybrid dynamical systems approach (Q6648476) (← links)
- Decentralized concurrent learning with coordinated momentum and restart (Q6648490) (← links)
