Learning Constrained Optimization with Deep Augmented Lagrangian Methods

arXiv:2403.03454v1 Announce Type: new
Abstract: Learning to Optimize (LtO) is a problem setting in which a machine learning (ML) model is trained to emulate a constrained optimization solver. Learning to produce optimal and feasible solutions subject to complex constraints is a difficult task, but is often made possible by restricting the input space to a limited distribution of related problems. Most LtO methods focus on directly learning solutions to the primal problem, and applying correction schemes or loss function penalties …

abstract arxiv constraints cs.lg machine machine learning math.oc optimization solutions solver type