Recent works in learning-integrated optimization have shown promise in settings where the optimization problem is only partially observed or where general-purpose optimizers perform poorly without expert tuning. By learning an optimizer \(\mathbf{g}\) to tackle these challenging problems with \(f\) as the objective, the optimization process can be...

Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information