  We derive a set of ptychography phase-retrieval iterative engines based on
proximal algorithms originally developed in convex optimization theory, and
discuss their connections with existing ones. The use of proximal operator
creates a simple frame work that allows us to incorporate the effect of noise
from a maximum-likelihood principle. We focus on three particular algorithms,
namely proximal minimization, alternating direction method of multiplier and
accelerated proximal gradient, and benckmark their performance with numerical
simulations and experimental x-ray data. Among them, accelerated proximal
gradient shows superior performance in terms of both accuracy and convergence
rate for a noisy dataset.
