Accelerating Quadratic Optimization Up to 3x With Reinforcement Learning | Synced

A research team from the University of California, Princeton University and ETH Zurich proposes RLQP, an accelerated QP solver based on operator-splitting QP (OSQP) that uses deep reinforcement lea...

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Source: Synced | AI Technology & Industry Review

A research team from the University of California, Princeton University and ETH Zurich proposes RLQP, an accelerated QP solver based on operator-splitting QP (OSQP) that uses deep reinforcement learning (RL) to speed up the solver’s convergence rate.