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
2 Pith papers cite this work. Polarity classification is still indexing.
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Gaussian mixture models combined with multiple local linearizations solve nonlinear stochastic density steering and yield provably tighter approximation bounds than single-linearization baselines.
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A Unified Control-Theoretic Framework for Saddle-Point Dynamics in Constrained Optimization
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.
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Nonlinear Stochastic Density Steering via Gaussian Mixture Schrodinger Bridges and Multiple Linearizations
Gaussian mixture models combined with multiple local linearizations solve nonlinear stochastic density steering and yield provably tighter approximation bounds than single-linearization baselines.