right, $\alpha \in \{1.0, 3.0, 6.0\}$. (Note that ConOpt and SGA coincide on a bilinear problem)[]{data-label="fig:bilinear_strong"}](figures/bilinear_strong_alpha1.png "fig:") ![The first 50 iterations of GDA, LCGD, ConOpt, OGDA, and CGD with parameters $\eta = 0.2$ and $\gamma = 1.0$. The objective function is $f(x,y) = \alpha x^{\top}y$ for, from left to right, $\alpha \in \{1.0, 3.0, 6.0\}$. (Note that ConOpt and SGA coincide on a bilinear problem)[]{data-label="fig:bilinear_strong"}](figures/bilinear_strong_alpha3.png "fig:") ![The first 50 iterations of GDA, LCGD, ConOpt, OGDA, and CGD with parameters $\eta = 0.2$ and $\gamma = 1.0$. The objective function is $f(x,y) = \alpha x^{\top}y$ for, from left to right, $\alpha \in \{1.0, 3.0, 6.0\}$. (Note that ConOpt and SGA coincide on a bilinear problem)[]{data-label="fig:bilinear_strong"}](figures/bilinear_strong_alpha6.png "fig:")

![We measure the (non-)convergence to equilibrium in the separable convex-concave– ($f(x,y) = \alpha( x^2 - y^2 )$, left three plots) and concave convex problem ($f(x,y) = \alpha( -x^2 + y^2 )$, right three plots), for $\alpha \in \{1.0,3.0,6.0\}$. (Color coding given by , , , the y-axis measures $\log_{10}(\|(x_{k},y_{k})\|)$ and the x-axis the number of iterations $k$. Note that convergence is desired for the first problem, while *divergence* is desired for the second problem.[]{data-label="fig:quad"}](figures/quadratic_equi_alpha1.png "fig:") ![We measure the (non-)convergence to equilibrium in the separable convex-concave– 