Finding Structure and Causality in Linear Programs
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Abstract (arXiv)
Linear Programs (LP) are celebrated widely, particularly so in machine learning where they have allowed for effectively solving probabilistic inference tasks or imposing structure on end-to-end learning systems. Their potential might seem depleted but we propose a foundational, causal perspective that reveals intriguing intra- and inter-structure relations for LP components. We conduct a systematic, empirical investigation on general-, shortest path- and energy system LPs.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 25 references that are in the map. First posted on arXiv 29 Mar 2022.
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References · 9
- 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
- 2019Differentiable Convex Optimization Layerscited by 458
- 2018Neural Relational Inference for Interacting Systemscited by 339
- 2016Foundations of Structural Causal Models with Cycles and Latent Variablescited by 134
- 2020Interior Point Solving for LP-based prediction+optimisationcited by 83
- 2020Learning with Differentiable Perturbed Optimizerscited by 81
- 2016Discovering Causal Signals in Imagescited by 79
- 2021CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraintscited by 49
- 2020Stronger and Faster Wasserstein Adversarial Attackscited by 25
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