Eigenvalue-Decomposition Cost Denoising Offered as Alternative to Predict-then-Optimize

A new arXiv paper proposes cleaning up predicted edge costs via eigenvalue decomposition before solving shortest-path problems, rather than feeding raw predictions straight into the optimizer. The approach is positioned as a substitute for predict-then-optimize pipelines like SPO+, which learn a mapping from contextual features to unknown costs and then optimize on those predictions. The authors argue this denoising step can improve outcomes on combinatorial problems where prediction error propagates into the final solution.