  The binary perceptron problem asks us to find a sign vector in the
intersection of independently chosen random halfspaces with intercept
$-\kappa$. We analyze the performance of the canonical discrepancy minimization
algorithms of Lovett-Meka and Rothvoss/Eldan-Singh for the asymmetric binary
perceptron problem. We obtain new algorithmic results in the $\kappa = 0$ case
and in the large-$|\kappa|$ case. In the $\kappa\to-\infty$ case, we
additionally characterize the storage capacity and complement our algorithmic
results with an almost-matching overlap-gap lower bound.
