  High-dimensional signal recovery of standard linear regression is a key
challenge in many engineering fields, such as, communications, compressed
sensing, and image processing. The approximate message passing (AMP) algorithm
proposed by Donoho \textit{et al} is a computational efficient method to such
problems, which can attain Bayes-optimal performance in independent identical
distributed (IID) sub-Gaussian random matrices region. A significant feature of
AMP is that the dynamical behavior of AMP can be fully predicted by a scalar
equation termed station evolution (SE). Although AMP is optimal in IID
sub-Gaussian random matrices, AMP may fail to converge when measurement matrix
is beyond IID sub-Gaussian. To extend the region of random measurement matrix,
an expectation propagation (EP)-related algorithm orthogonal AMP (OAMP) was
proposed, which shares the same algorithm with EP, expectation consistent (EC),
and vector AMP (VAMP). This paper aims at giving a review for those algorithms.
We begin with the worst case, i.e., least absolute shrinkage and selection
operator (LASSO) inference problem, and then give the detailed derivation of
AMP derived from message passing. Also, in the Bayes-optimal setting, we give
the Bayes-optimal AMP which has a slight difference from AMP for LASSO. In
addition, we review some AMP-related algorithms: OAMP, VAMP, and Memory AMP
(MAMP), which can be applied to more general random matrices.
