Quantitative Sensitivity Bounds for Nonlinear Programming and Time-varying Optimization

Inspired by classical sensitivity results for nonlinear optimization, we derive and discuss new quantitative bounds to characterize the solution map dual variables of a parametrized program. In particular, explicit expressions local global Lipschitz constants nonconvex or convex optimization problems, respectively. Our are geared towards study time-varying which commonplace in various applications online including power systems, robotics, signal processing, more. this context, our can be used bound rate change optimizer. To illustrate use generalize existing arguments quantify tracking performance continuous-time, monotone running algorithms. Furthermore, introduce continuous-time algorithm constrained model as so-called perturbed sweeping process. For discontinuous scheme establish an on asymptotic class problems.