  We provide a new perspective on the study of parameterized optimization
problems. Our approach combines methods for post-optimal sensitivity analysis
and ordinary differential equations to quantify the uncertainty in the
minimizer due to uncertain parameters in the optimization problem. We
illustrate the proposed approach with a simple analytic example and an inverse
problem governed by an advection diffusion equation.
