Abstract
In this paper we present an algorithm which allows the approximation of a constrained saddle point problem by a sequence of unconstrained and finite dimensional optimization problems using an exact penalty functional for constrained minimax problems. Numerical experiments, both in finite and infinite dimension, are reported and are compared with those obtained by the use of an external penalization.
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