We show (under standard assumptions) that there are NP optimization problems for which estimation is easier than approximation. Namely, one can estimate the value of the optimal solution within a ratio of $\rho$, but it is difficult to find a solution whose value is within $\rho$ of optimal.
As an ... more >>>

Given a random permutation $f: [N] \to [N]$ as a black box and $y \in [N]$, we want to output $x = f^{-1}(y)$. Supplementary to our input, we are given classical advice in the form of a pre-computed data structure; this advice can depend on the permutation but \emph{not} on ... more >>>

We prove that if the hardness of inverting a size-verifiable one-way function can
be based on NP-hardness via a general (adaptive) reduction, then coAM is contained in NP. This
claim was made by Akavia, Goldreich, Goldwasser, and Moshkovitz (STOC 2006), but
was later retracted (STOC 2010).

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