Title: Approximate message passing for nonconvex sparse regularization with stability and asymptotic analysis,
Abstract: We analyse a linear regression problem with nonconvex regularization called
smoothly clipped absolute deviation (SCAD) under an overcomplete Gaussian basis
for Gaussian random data. We propose an approximate message passing (AMP)
algorithm considering nonconvex regularization, namely SCAD-AMP, and
analytically show that the stability condition corresponds to the de
Almeida--Thouless condition in spin glass literature. Through asymptotic
analysis, we show the correspondence between the density evolution of SCAD-AMP
and the replica symmetric solution. Numerical experiments confirm that for a
sufficiently large system size, SCAD-AMP achieves the optimal performance
predicted by the replica method. Through replica analysis, a phase transition
between replica symmetric (RS) and replica symmetry breaking (RSB) region is
found in the parameter space of SCAD. The appearance of the RS region for a
nonconvex penalty is a significant advantage that indicates the region of
smooth landscape of the optimization problem. Furthermore, we analytically show
that the statistical representation performance of the SCAD penalty is better
than that of L1-based methods, and the minimum representation error under RS
assumption is obtained at the edge of the RS/RSB phase. The correspondence
between the convergence of the existing coordinate descent algorithm and RS/RSB
transition is also indicated.