  In this paper, for solving large-scale nonlinear equations we propose a
nonlinear sampling Kaczmarz-Motzkin (NSKM) method. Based on the local
tangential cone condition and the Jensen's inequality, we prove convergence of
our method with two different assumptions. Then, for solving nonlinear
equations with the convex constraints we present two variants of the NSKM
method: the projected sampling Kaczmarz-Motzkin (PSKM) method and the
accelerated projected sampling Kaczmarz-Motzkin (APSKM) method. With the use of
the nonexpansive property of the projection and the convergence of the NSKM
method, the convergence analysis is obtained. Numerical results show that the
NSKM method with the sample of the suitable size outperforms the nonlinear
randomized Kaczmarz (NRK) method in terms of calculation times. The APSKM and
PSKM methods are practical and promising for the constrained nonlinear problem.
