  The generalized Kullback-Leibler divergence (K-Ld) in Tsallis statistics
[constrained by the additive duality of generalized statistics (dual
generalized K-Ld)] is here reconciled with the theory of Bregman divergences
for expectations defined by normal averages, within a measure-theoretic
framework. Specifically, it is demonstrated that the dual generalized K-Ld is a
scaled Bregman divergence. The Pythagorean theorem is derived from the minimum
discrimination information-principle using the dual generalized K-Ld as the
measure of uncertainty, with constraints defined by normal averages. The
minimization of the dual generalized K-Ld, with normal averages constraints, is
shown to exhibit distinctly unique features.
