Abstract
AbstractOf central importance in convex analysis are conditions guaranteeing that the conjugate of a sum is the infimal convolution of the conjugates. The main result in this direction is a theorem due to Attouch and Br9zis. In turn, it gives rise to the Fenchel–Rockafellar duality framework for convex optimization problems. The applications we discuss include von Neumann’s minimax theorem as well as several results on the closure of the sum of linear subspaces.KeywordsDual ProblemLinear SubspaceProper FunctionPrimal ProblemConvex Optimization ProblemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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