Covariance-Aware Transformers for Quadratic Programming and Decision Making
This work shows how transformer architectures can be explicitly designed to solve various quadratic programs (QPs) by emulating classical optimization algorithms like gradient descent. By feeding covariance matrix information into these models via 'Time2Decide', the system achieves end-to-end decision-making, outperforming traditional methods in portfolio optimization by learning complex structural constraints in a single pass.