  We develop a trust-region method for minimizing the sum of a smooth term $f$
and a nonsmooth term $h$), both of which can be nonconvex. Each iteration of
our method minimizes a possibly nonconvex model of $f + h$ in a trust region.
The model coincides with $f + h$ in value and subdifferential at the center. We
establish global convergence to a first-order stationary point when $f$
satisfies a smoothness condition that holds, in particular, when it has
Lipschitz-continuous gradient, and $h$ is proper and lower semi-continuous. The
model of $h$ is required to be proper, lower-semi-continuous and prox-bounded.
Under these weak assumptions, we establish a worst-case $O(1/\epsilon^2)$
iteration complexity bound that matches the best known complexity bound of
standard trust-region methods for smooth optimization. We detail a special
instance, named TR-PG, in which we use a limited-memory quasi-Newton model of
$f$ and compute a step with the proximal gradient method, resulting in a
practical proximal quasi-Newton method. We establish similar convergence
properties and complexity bound for a quadratic regularization variant, named
R2, and provide an interpretation as a proximal gradient method with adaptive
step size for nonconvex problems. R2 may also be used to compute steps inside
the trust-region method, resulting in an implementation named TR-R2. We
describe our Julia implementations and report numerical results on inverse
problems from sparse optimization and signal processing. Both TR-PG and TR-R2
exhibit promising performance and compare favorably with two linesearch
proximal quasi-Newton methods based on convex models.
