  In this work, we focuses on the following saddle point problem $\min_x \max_y
p(x) + R(x,y) - q(y)$ where $R(x,y)$ is $L_R$-smooth, $\mu_x$-strongly convex,
$\mu_y$-strongly concave and $p(x), q(y)$ are convex and $L_p, L_q$-smooth
respectively. We present a new algorithm with optimal overall complexity
$\mathcal{O}\left(\left(\sqrt{\frac{L_p}{\mu_x}} + \frac{L_R}{\sqrt{\mu_x
\mu_y}} + \sqrt{\frac{L_q}{\mu_y}}\right)\log \frac{1}{\varepsilon}\right)$ and
separation of oracle calls in the composite and saddle part. This algorithm
requires $\mathcal{O}\left(\left(\sqrt{\frac{L_p}{\mu_x}} +
\sqrt{\frac{L_q}{\mu_y}}\right) \log \frac{1}{\varepsilon}\right)$ oracle calls
for $\nabla p(x)$ and $\nabla q(y)$ and $\mathcal{O} \left(
\max\left\{\sqrt{\frac{L_p}{\mu_x}}, \sqrt{\frac{L_q}{\mu_y}},
\frac{L_R}{\sqrt{\mu_x \mu_y}} \right\}\log \frac{1}{\varepsilon}\right)$
oracle calls for $\nabla R(x,y)$ to find an $\varepsilon$-solution of the
problem. To the best of our knowledge, we are the first to develop optimal
algorithm with complexity separation in the case $\mu_x \not = \mu_y$. Also, we
apply this algorithm to a bilinear saddle point problem and obtain the optimal
complexity for this class of problems.
