Abstract
A fundamental question in complexity theory is whether every randomized polynomial time algorithm can be simulated by a deterministic polynomial time algorithm (that is, whether BPP=P). A beautiful theory of derandomization was developed in recent years in attempt to solve this problem. In this article we survey some recent work on relaxed notions of derandomization that allow the deterministic simulation to err on some inputs. We use this opportunity to also provide a brief overview to some results and research directions in "classical derandomization".
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