Extending the Scope of Robust Quadratic Optimization

We derive computationally tractable formulations of the robust counterparts convex quadratic and conic constraints that are concave in matrix-valued uncertain parameters. do this for a broad range uncertainty sets. Our results provide extensions to known from literature. also consider hard constraints: those For counterpart such constraints, we inner outer approximations. As an application, show how construct natural set based on statistical confidence around sample mean vector covariance matrix use reformulation portfolio optimization problem. apply paper norm approximation problems. Summary Contribution: This develops new theoretical algorithms extend scope More specifically,