Diffusing-horizon MPC applies exponentially sparse time grids to linear constrained MPC, leveraging exponential sensitivity decay to reduce solve time by two orders of magnitude with 3% cost penalty in an HVAC case study.
Distributed optimization and statistical learning via the alternating direction method of multipliers,
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The paper proposes Consensus ALADIN (C-ALADIN) algorithms that solve distributed consensus optimization with global convergence for convex problems and local convergence for non-convex ones, including a decentralized version over directed graphs using quantized communication.
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Diffusing-Horizon Model Predictive Control
Diffusing-horizon MPC applies exponentially sparse time grids to linear constrained MPC, leveraging exponential sensitivity decay to reduce solve time by two orders of magnitude with 3% cost penalty in an HVAC case study.
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Distributed and Decentralized Optimization Algorithms via Consensus ALADIN
The paper proposes Consensus ALADIN (C-ALADIN) algorithms that solve distributed consensus optimization with global convergence for convex problems and local convergence for non-convex ones, including a decentralized version over directed graphs using quantized communication.