Jeremy Bertoncini

Regularized interior-point solvers for convex quadratic programs have long relied on hand-set regularization weights, barrier parameters and inner tolerances. RL-QProxIP uses a reinforcement-learning policy to output contraction factors for those parameters at each outer iteration without changing the underlying algorithm, so the solver keeps its convergence guarantees. On the Maros–Mészáros test set the policy is the fastest solver on about 70% of instances, while the base QProxIP needs about 3.5 times the time to reach the same fraction; the source states two conflicting versions of their final coverage, so no preference is taken here.

Regularized interior-point solvers for convex quadratic programs have long relied on hand-set regularization weights, barrier parameters and inner tolerances. RL-QProxIP uses a reinforcement-learning policy to output contraction factors for those parameters at each outer iteration without changing the underlying algorithm, so the solver keeps its convergence guarantees. On the Maros–Mészáros test set the policy is the fastest solver on about 70% of instances, while the base QProxIP needs about 3.5 times the time to reach the same fraction; the source states two conflicting versions of their final coverage, so no preference is taken here.