[2608.14472] LP-NAS: Linear Programming-based Neural Architecture Search Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2608.14472 (cs) [Submitted on 14 Aug 2026] Title: LP-NAS: Linear Programming-based Neural Architecture Search Authors: Abhishek Shukla , Ankur Sinha , Faiz Hamid View a PDF of the paper titled LP-NAS: Linear Programming-based Neural Architecture Search, by Abhishek Shukla and 2 other authors View PDF HTML (experimental) Abstract: Neural Architecture Search (NAS) aims to automate neural network architecture design, reducing reliance on human expertise. Among the various NAS methods, differentiable NAS has gained prominence due to its efficiency and accuracy compared to conventional NAS approaches. Since differentiable NAS relaxes the architecture search space into a continuous domain, it is possible to apply principles from continuous optimization to NAS. In this paper, we propose Linear Programming-based NAS (LP-NAS), a mathematical programming-based framework for differentiable NAS that is applicable to a wide range of continuous search spaces. LP-NAS formulates a linear program (LP) using the validation-loss gradient and the training-loss Hessian to compute an architecture update direction that improves generalization while preserving the optimality of the model parameters. By following this LP-derived descent direction, LP-NAS efficiently navigates the architecture search space, leading to faster and more effective architecture optimization. We introduce two computationally efficient variants of LP-NAS, namely S-LP-NAS and R-LP-NAS. Applying LP-NAS to the Differentiable Architecture Search (DARTS) search space results in two algorithmic variants, S-LP-DARTS and R-LP-DARTS. Both variants achieve faster convergence and significantly higher validation performance during the early search iterations than the standard DARTS algorithm. Extensive experiments on CIFAR-10 and CIFAR-100 show that LP-DARTS outperforms standard DARTS in both the architecture search and evaluation phases. Additionally, we compare our approach with several DARTS variants (P-DARTS, PC-DARTS, and STO-DARTS) on the CIFAR-10 dataset and demonstrate its effectiveness. Furthermore, we validate the transferability of the discovered architectures through experiments on the ImageNet dataset. Comments: 20 pages, 5 figures Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2608.14472 [cs.LG] (or arXiv:2608.14472v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.14472 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ankur Sinha PhD [ view email ] [v1] Fri, 14 Aug 2026 16:53:11 UTC (4,573 KB) Full-text links: Access Paper: View a PDF of the paper titled LP-NAS: Linear Programming-based Neural Architecture Search, by Abhishek Shukla and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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