A delayed stochastic swarm model with singular repulsion is made rigorous via an augmented Lyapunov functional, then optimized to show temporally sparse bang-off-bang leader actuation reduces effort and that more leaders are not always better.
SIAM Journal on Optimization17(4), 969–996 (2007)
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PECO strengthens chance constraints by mandating feasibility for all high-probability events and is solved via a data-embedded deterministic program that works for nonlinear nonconvex instances when the size of the solution-determining data family can be estimated by machine learning.
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Controlling the Swarm: Sparse Actuation and Collision Avoidance under Stochastic Delay
A delayed stochastic swarm model with singular repulsion is made rigorous via an augmented Lyapunov functional, then optimized to show temporally sparse bang-off-bang leader actuation reduces effort and that more leaders are not always better.
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A Data-embedded Solution Paradigm for Nonconvex Probable Event Constrained Optimization
PECO strengthens chance constraints by mandating feasibility for all high-probability events and is solved via a data-embedded deterministic program that works for nonlinear nonconvex instances when the size of the solution-determining data family can be estimated by machine learning.