Approximate Message Passing with Random Initialization for Phase Retrieval
Yuchen Chen, Yandi Shen, Xingyu Xu
Abstract
We analyze approximate message passing (AMP) with an independent Gaussian initialization for noiseless phase retrieval in the proportional asymptotic regime. A random initialization has overlap of order d-1/2 with the signal, and AMP requires a growing number of iterations to attain non-vanishing overlap. Thus, its precise behavior cannot be characterized by classical fixed-time state evolution. We prove a Gaussian decomposition of the AMP trajectory and control its error over the horizons required for recovery. The resulting analysis shows that random initialization attains the weak-recovery threshold δ weak=1/2. For δ∈(δ weak,δ str), where δ str≈1.13, the signal strength follows state evolution and approaches its stable finite fixed point uniformly for \(n1/3/polylog(n)\) iterations. For δ>δ str, AMP reaches any prescribed fixed recovery accuracy within Oδ,( n) iterations. The majority of our analysis applies more generally to generalized AMP for single-index models.
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