This article provides results guarateeing that the optimal value of a given convex infinite optimization problem and its corresponding surrogate Lagrangian dual coincide and the primal optimal value is attainable. The conditions ensuring converse strong Lagrangian (in short, minsup) duality involve the weakly-inf-(locally) compactness of suitable functions and the linearity or relative closedness of some sets depending on the data. Applications are given to different areas of convex optimization, including an extension of the Clark-Duffin Theorem for ordinary convex programs.

Contact details are reproduced from the original publication and may be historical.

Miguel A. Goberna

Dept. of Statistics and Operations Research, University of Alicante, Apt. de Correos 99, 03080 Alicante, Spain

mgoberna@ua.es

Marco Antonio López

Dept. of Statistics and Operations Research, University of Alicante, Apt. de Correos 99, 03080 Alicante, Spain

marco.antonio@ua.es

M. A. Goberna, M. A. López, M. Volle. “Primal Attainment in Convex Infinite Optimization Duality.” Journal of Convex Analysis 21 (2014), No. 4, 1043–1064.