A Function Approximation Approach for Parametric Optimization

Abstract We present a novel approach for approximating the primal and dual parameter-dependent solution functions of parametric optimization problems. start with an equation reformulation first-order necessary optimality conditions. Then, we replace solutions some find test parameters optimal coefficients as single nonlinear least-squares problem. Under mild assumptions it can be shown that stationary points are global minima function approximations interpolate at all parameters. Further, have cheap evaluation criterion to estimate approximation error. Finally, preliminary numerical results showing viability our approach.