Work by Shane Barratt, Guillermo Angeris, Stephen Boyd, 2020

Keywords: Convex, optimization, feasible, design

Summary

This work looks at the meta-optimization setting where the original convex problem is infeasible, unbounded, or pathological (bad) and the problem is changed by a small (minimum) amount to become feasible, bounded, and nonpathological (good). The problem parameter perturbation is itself minimized by the meta-optimization. The authors give examples from control and economic theory, showing that their method can be used as a design tool e.g. slightly changing the mass properties of an aerospace vehicle such that a constrained trajectory planning problem becomes feasible.

Read the paper on arXiv here.