Octav Olteanu, Janina Mihaela Mih

A constrained optimization problem is solved, as an application of minimum principle for a sum of strictly concave continuous functions, subject to a linear constraint, firstly for finite sums of elementary such functions. The motivation of solving such problems is minimizing and evaluating the (unknown) mean of a random variable, in terms of the (known) mean of another related random variable. The corresponding result for infinite sums of such type of functions follows as a consequence, passing to the limit. Note that in our statements and proofs the condition Σ


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