Dear Colleagues:

It is our pleasure to announce that the following paper was named the winner of the 2015 OPTL Best Paper Award:

V. Jeyakumar, G. Li, J. Vicente-Pérez (2015), Robust SOS-convex polynomial optimization problems: exact SDP relaxations, Optimization Letters 9(1), 1–18.

Abstract   This paper studies robust solutions and semidefinite linear programming (SDP) relaxations of a class of convex polynomial optimization problems in the face of data uncertainty. The class of convex optimization problems, called robust SOS-convex polynomial optimization problems, includes robust quadratically constrained convex optimization problems and robust separable convex polynomial optimization problems. It establishes sums-of-squares polynomial representations characterizing robust solutions and exact SDP-relaxations of robust SOS-convex polynomial optimization problems under various commonly used uncertainty sets. In particular, the results show that the polytopic and ellipsoidal uncertainty sets, that allow second-order cone re-formulations of robust quadratically constrained optimization problems, continue to permit exact SDP-relaxations for a broad class of robust SOS-convex polynomial optimization problems.

Please join us in congratulating Dr. Jeyakumar, Dr. Li and Dr. Vicente-Pérez for their excellent contribution to literature!

The OPTL Best Paper Award carries a 1,000 USD prize and a plaque. This year the Best Paper Award Selection Committee included OPTL Editorial Board members Dr. Boris Mordukhovich, Dr. Jean-Philippe Richard, and the Founding Editor-In-Chief, Dr. Panos Pardalos. We want to thank the members of the Committee for their service and Springer for sponsoring the award. We also would like to take this opportunity to invite nominations for the 2016 OPTL Best Paper Award. All papers published in OPTL during the year of 2016 are eligible.