Robust solutions of nonlinear least squares problems via min-max optimization

Abstract This paper considers robust solutions to a class of nonlinear least squares problems using a min-max optimization approach. We give an explicit formula for the value function of the inner maximization problem and show the existence of global minimax points. We establish error bounds from any solution of the nonlinear least squares problem to the solution set of the robust nonlinear least squares problem. Moreover, we propose a smoothing method for finding a global minimax point of the min-max problem by using the formula and show that finding an $\varepsilon $ minimax critical point of the min-max problem needs at most $O(\varepsilon ^{-2} +\delta ^{2} \varepsilon ^{-3})$ evaluations of the function value and gradients of the objective function, where $\delta $ is the tolerance of the noise. Numerical results of integral equations with uncertain data demonstrate the robustness of solutions of our approach and unstable behavior of least squares solutions disregarding uncertainties in the data.