Presents a successive convexification framework that enforces continuous-time STL specifications in trajectory optimization via GMSR robustness and prox-convex solving.
Varma, and Antoine O
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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.
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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Successive Convexification for Trajectory Optimization with Continuous-time Satisfaction of Signal Temporal Logic Specifications
Presents a successive convexification framework that enforces continuous-time STL specifications in trajectory optimization via GMSR robustness and prox-convex solving.
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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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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.
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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.
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Boundary bilinear control of semilinear parabolic PDEs: quadratic convergence of the SQP method
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.