  We consider the minimization problems of the form $P(\varphi, g, h)$:
$\min\{f(x) = \varphi(x) + g(x) - h(x): x \in \Bbb R^n\}$, where $\varphi$ is a
differentiable function and $g$, $h$ are convex functions, and introduce
iterative methods to finding a critical point of $f$ when $f$ is
differentiable. We show that the point computed by proximal point algorithm at
each iteration can be used to determine a descent direction for the objective
function at this point. This algorithm can be considered as a combination of
proximal point algorithm together with a linesearch step that uses this descent
direction. We also study convergence results of these algorithms and the
inertial proximal methods proposed by P.E. Maing$\acute{e}$ {\it et.al.}
\cite{MM} under the main assumption that the objective function satisfies the
Kurdika-{\L}ojasiewicz property.
