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تسجيل مستخدم جديدAll-or-Nothing Phenomena: From Single-Letter to High Dimensions
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We consider the linear regression problem of estimating a $p$-dimensional vector $beta$ from $n$ observations $Y = X beta + W$, where $beta_j stackrel{text{i.i.d.}}{sim} pi$ for a real-valued distribution $pi$ with zero mean and unit variance, $X_{ij} stackrel{text{i.i.d.}}{sim} mathcal{N}(0,1)$, and $W_istackrel{text{i.i.d.}}{sim} mathcal{N}(0, sigma^2)$. In the asymptotic regime where $n/p to delta$ and $ p/ sigma^2 to mathsf{snr}$ for two fixed constants $delta, mathsf{snr}in (0, infty)$ as $p to infty$, the limiting (normalized) minimum mean-squared error (MMSE) has been characterized by the MMSE of an associated single-letter (additive Gaussian scalar) channel. In this paper, we show that if the MMSE function of the single-letter channel converges to a step function, then the limiting MMSE of estimating $beta$ in the linear regression problem converges to a step function which jumps from $1$ to $0$ at a critical threshold. Moreover, we establish that the limiting mean-squared error of the (MSE-optimal) approximate message passing algorithm also converges to a step function with a larger threshold, providing evidence for the presence of a computational-statistical gap between the two thresholds.
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