  We propose a path optimization method (POM) to evade the sign problem in the
Monte-Carlo calculations for complex actions. Among many approaches to the sign
problem, the Lefschetz-thimble path-integral method and the complex Langevin
method are promising and extensively discussed. In these methods, real field
variables are complexified and the integration manifold is determined by the
flow equations or stochastically sampled. When we have singular points of the
action or multiple critical points near the original integral surface, however,
we have a risk to encounter the residual and global sign problems or the
singular drift term problem. One of the ways to avoid the singular points is to
optimize the integration path which is designed not to hit the singular points
of the Boltzmann weight. By specifying the one-dimensional integration-path as
$z=t+if(t) (f\in \mathbb{R})$ and by optimizing $f(t)$ to enhance the average
phase factor, we demonstrate that we can avoid the sign problem in a
one-variable toy model for which the complex Langevin method is found to fail.
In this proceedings, we propose POM and discuss how we can avoid the sign
problem in a toy model. We also discuss the possibility to utilize the neural
network to optimize the path.
