SOURCE-LINKED INTELLIGENCE

Equilibrium bias and convergence in augmented primal--dual dynamics with sampled constraints

arXiv · AI, language, vision and robotics · article · Sep 12, 2026 · UTC

This work studies the stability and convergence of augmented primal-dual dynamics when constraint values are estimated from samples. Unbiased constraint observations can produce a biased augmented multiplier signal, shifting the equilibria of the mean dynamics. For componentwise inequalities, we give a necessary and sufficient condition for preserving the Karush-Kuhn-Tucker (KKT) equilibria and construct a convex example with a locally exponentially stable equilibrium that violates complementarity. To address this bias, constraint values are estimated recursively before forming the augmented m

Read original source ↗ Open in workspace

recordType

paper

region

Global