  We propose an approach to saddle point optimization relying only on oracles
that solve minimization problems approximately. We analyze its convergence
property on a strongly convex--concave problem and show its linear convergence
toward the global min--max saddle point. Based on the convergence analysis, we
develop a heuristic approach to adapt the learning rate. An implementation of
the developed approach using the (1+1)-CMA-ES as the minimization oracle,
namely Adversarial-CMA-ES, is shown to outperform several existing approaches
on test problems. Numerical evaluation confirms the tightness of the
theoretical convergence rate bound as well as the efficiency of the learning
rate adaptation mechanism. As an example of real-world problems, the suggested
optimization method is applied to automatic berthing control problems under
model uncertainties, showing its usefulness in obtaining solutions robust to
uncertainty.
