Target Mirror Descent unifies and stabilizes algorithms for monotone variational inequalities via target point correction in the dual update, recovering proximal point, extragradient, and other methods as special cases while supporting geometric ensembles.
Equilibrium-independent passivity: A new definition and numerical certification
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Goal-conditioned neural ODEs built from bi-Lipschitz diffeomorphisms deliver global exponential stability and safe-set invariance for all-pairs motion planning with explicit convergence bounds.
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Target Mirror Descent: A Unifying Framework for Solving Monotone Variational Inequalities
Target Mirror Descent unifies and stabilizes algorithms for monotone variational inequalities via target point correction in the dual update, recovering proximal point, extragradient, and other methods as special cases while supporting geometric ensembles.
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Goal-Conditioned Neural ODEs with Guaranteed Safety and Stability for Learning-Based All-Pairs Motion Planning
Goal-conditioned neural ODEs built from bi-Lipschitz diffeomorphisms deliver global exponential stability and safe-set invariance for all-pairs motion planning with explicit convergence bounds.