Uncountably many maximizing measures for a dense subset of continuous functions

Ergodic optimization aims to single out dynamically invariant Borel probability measures which maximize the integral of a given 'performance' function. For a continuous self-map of a compact metric space and a dense set of continuous functions, we show the existence of uncountably many ergodic maximizing measures. We also show that, for a topologically mixing subshift of finite type and a dense set of continuous functions there exist uncountably many ergodic maximizing measures with full support and positive entropy.