Generalized Kullback-Leibler Divergence Minimization within a Scaled Bregman Framework

The generalized Kullback-Leibler divergence (K-Ld) in Tsallis statistics subjected to the additive duality of generalized statistics (dual generalized K-Ld) is 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 possess distinctly unique features.