In sparse linear regression, the SLOPE estimator generalizes LASSO by assigning magnitude-dependent regularizations to different coordinates of the estimate. In our new paper, we present an asymptotically exact characterization of the performance of SLOPE in the high-dimensional regime. Our asymptotic characterization enables us to derive optimal regularization sequences to either minimize the MSE or to maximize the power in variable selection under any given level of Type-I error.