Theory and Applications of Probabilistic Kolmogorov Complexity
Diverse applications of Kolmogorov complexity to learning [CIKK16], circuit complexity [OPS19], cryptography [LP20], average-case complex- ity [Hir21], and proof search [Kra22] have been discovered in recent years. Since the running time of algorithms is a key resource in these fields, it is crucial in the corresponding arguments to consider time-bounded variants of Kolmogorov complexity. While fruitful interactions between time-bounded Kolmogorov complexity and different areas of theoretical computer science have been known for quite a while (e.g., [Sip83, Ko91, ABK+06, AF09], to name a few), the aforementioned results have led to a renewed interest in this topic.
The theory of Kolmogorov complexity is well understood, but many use- ful results and properties of Kolmogorov complexity are not known to hold in time-bounded settings. Unfortunately, this creates technical difficulties or leads to conditional results when applying methods from time-bounded Kolmogorov complexity to algorithms and complexity theory. Perhaps even more importantly, in many cases it is desirable or even necessary to con- sider randomised algorithms. Since random strings have high complexity, the classical theory of time-bounded Kolmogorov complexity might be in- appropriate or simply cannot be applied in such contexts.
To mitigate these issues and develop a more robust theory of time-bounded Kolmogorov complexity that survives in the important setting of randomised computations, some recent papers [Oli19, LO21, LOS21, GKLO22, LOZ22] have explored probabilistic notions of time-bounded Kolmogorov complex- ity, such as rKt complexity [Oli19], rKt complexity [LOS21], and pKt com- plexity [GKLO22]. These measures consider different ways of encoding an object via a probabilistic representation. In this survey, we provide an introduction to probabilistic time-bounded Kolmogorov complexity and its applications, highlighting many open problems and research directions.
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