We study the problem of reconstructing a perfect matching $M^*$ hidden in a randomly weighted $ntimes n$ bipartite graph. The edge set includes every node pair in $M^*$ and each of the $n(n-1)$ node pairs not in $M^*$ independently with probability $d/n$. The weight of each edge $e$ is independently drawn from the distribution $mathcal{P}$ if $e in M^*$ and from $mathcal{Q}$ if $e otin M^*$. We show that if $sqrt{d} B(mathcal{P},mathcal{Q}) le 1$, where $B(mathcal{P},mathcal{Q})$ stands for the Bhattacharyya coefficient, the reconstruction error (average fraction of misclassified edges) of the maximum likelihood estimator of $M^*$ converges to $0$ as $nto infty$. Conversely, if $sqrt{d} B(mathcal{P},mathcal{Q}) ge 1+epsilon$ for an arbitrarily small constant $epsilon>0$, the reconstruction error for any estimator is shown to be bounded away from $0$ under both the sparse and dense model, resolving the conjecture in [Moharrami et al. 2019, Semerjian et al. 2020]. Furthermore, in the special case of complete exponentially weighted graph with $d=n$, $mathcal{P}=exp(lambda)$, and $mathcal{Q}=exp(1/n)$, for which the sharp threshold simplifies to $lambda=4$, we prove that when $lambda le 4-epsilon$, the optimal reconstruction error is $expleft( - Theta(1/sqrt{epsilon}) right)$, confirming the conjectured infinite-order phase transition in [Semerjian et al. 2020].
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