A PID feedback law on dual variables induces a unified family of saddle-point flows for constrained optimization, with explicit global exponential convergence guarantees under convexity and affine constraints.
Steering Large Agent Populations Using Mean-Field Schrödinger Bridges With Gaussian Mixture Models
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Characterizes the distributional mean-field limit of co-evolving latent space networks with feedback, including empirical measures and graphon convergence, via a conditional propagation of chaos result.
Gaussian mixture models combined with multiple local linearizations solve nonlinear stochastic density steering and yield provably tighter approximation bounds than single-linearization baselines.
Proves stability and quadratic convergence of an SQP algorithm for boundary bilinear control of semilinear parabolic PDEs under no-gap second-order sufficient optimality and strict complementarity conditions.
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Mean-Field Analysis of Latent Variable Process Models on Dynamically Evolving Graphs with Feedback Effects
Characterizes the distributional mean-field limit of co-evolving latent space networks with feedback, including empirical measures and graphon convergence, via a conditional propagation of chaos result.