We define a hierarchy of complexity classes that lie between P and RP, yielding a new way of quantifying partial progress towards the derandomization of RP. A standard approach in derandomization is to reduce the number of random bits an algorithm uses. We instead focus on a model of computation ... more >>>

Pseudodeterministic algorithms are probabilistic algorithms that solve search problems but do so by always providing the same (``canonical'') solution to a given instance, except with small probability.
While the complexity theoretic implications of pseudodeterministic algorithms were explored in the past, we suggest to conduct this exploration within the framework ... more >>>