UBC Mathematics Department
The first talk explained that (computational) hardness can be effectively turned into (computational) randomness. In this talk we will be stingy and ask exactly how hard (and how easy) must a function be, so as to achieve complete or partial derandomization of all efficient probabilistic algorithms. Successively weakening the hardness assumptions should be viewed as part of a program leading (hopefully) to obtaining UNCONDITIONAL limits on the power of randomness, such as e.g. BPP != EXP. I will talk mostly on one of the following two recent works with Russell Impagliazzo.
The first work deals with non-uniform hardness assumptions. Here we achieve a tight trade-off: exponential circuit lower bounds on any problem in E suffice to prove BPP=P. The improvement over previous trade-offs come from a solution to another problem of independent interest: deterministic amplification of hardness. This construction turns a hard-on-average Boolean function on n bits into a function 100n bits that is nearly unpredictable.
The second work deals with uniform hardness assumptions. Derandomization under such assumptions was known only for one-way functions. We show that at least for derandomizing algorithms in AvRP (namely probabilistic algorithms that work well for random inputs), it suffices to assume BPP != EXP. The obvious difficulty is that the conversion of a distinguisher of the Nisan-Wigderson generator (which is the only known tool that can use hard functions that are not 1-way) into an efficient algorithm for the hard function it is based on looks inherently non-uniform. We overcome this difficulty using a novel bootstrapping strategy.