{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/symilo-a-symmetry-aware-learning-framework","title":"SymILO: A Symmetry-Aware Learning Framework for Integer Linear Optimization","arxiv_id":"2409.19678","date":"2024-09-29","proceeding":null,"authors":["Qian Chen","Tianjian Zhang","Linxin Yang","Qingyu Han","Akang Wang","Ruoyu Sun","Xiaodong Luo","Tsung-Hui Chang"],"abstract":"Integer linear programs (ILPs) are commonly employed to model diverse practical problems such as scheduling and planning. Recently, machine learning techniques have been utilized to solve ILPs. A straightforward idea is to train a model via supervised learning, with an ILP as the input and an optimal solution as the label. An ILP is symmetric if its variables can be permuted without changing the problem structure, resulting in numerous equivalent and optimal solutions. Randomly selecting an optimal solution as the label can introduce variability in the training data, which may hinder the model from learning stable patterns. In this work, we incorporate the intrinsic symmetry of ILPs and propose a novel training framework called SymILO. Specifically, we modify the learning task by introducing solution permutation along with neural network weights as learnable parameters and then design an alternating algorithm to jointly optimize the loss function. We conduct extensive experiments on ILPs involving different symmetries and the computational results demonstrate that our symmetry-aware approach significantly outperforms three existing methods -- achieving $50.3\\%$, $66.5\\%$, and $45.4\\%$ average improvements, respectively.","url_abs":"https://arxiv.org/abs/2409.19678v3","url_pdf":"https://arxiv.org/pdf/2409.19678v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"symilo-a-symmetry-aware-learning-framework","repo_url":"https://github.com/netsysopt/symilo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2409.19678","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.19678"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/netsysopt/symilo","reach":{"status":"ok"}}],"summary":{"ran":7},"by_repo_kind":{"official":{"samples":7,"ran":7,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":7,"samples":[{"code_sha256_prefix":"78f423549ce09d0b","entry":"PF","repo":"netsysopt/symilo","repo_kind":"official","path":"utils.py","file_url":"https://github.com/netsysopt/symilo/blob/HEAD/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"78f423549ce09d0b"}},{"code_sha256_prefix":"7531b0dfc7e564d2","entry":"addPosFeatureSMSP","repo":"netsysopt/symilo","repo_kind":"official","path":"utils.py","file_url":"https://github.com/netsysopt/symilo/blob/HEAD/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"7531b0dfc7e564d2"}},{"code_sha256_prefix":"42d094052a22eae2","entry":"getPrimals","repo":"netsysopt/symilo","repo_kind":"official","path":"primals.py","file_url":"https://github.com/netsysopt/symilo/blob/HEAD/primals.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"42d094052a22eae2"}},{"code_sha256_prefix":"bcdf88584edf2dad","entry":"labelOpt","repo":"netsysopt/symilo","repo_kind":"official","path":"label_opt.py","file_url":"https://github.com/netsysopt/symilo/blob/HEAD/label_opt.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"bcdf88584edf2dad"}},{"code_sha256_prefix":"317a4f3db155d66e","entry":"lexOpt","repo":"netsysopt/symilo","repo_kind":"official","path":"label_opt.py","file_url":"https://github.com/netsysopt/symilo/blob/HEAD/label_opt.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"317a4f3db155d66e"}},{"code_sha256_prefix":"807ed19e0967f423","entry":"parseLog","repo":"netsysopt/symilo","repo_kind":"official","path":"get_primals.py","file_url":"https://github.com/netsysopt/symilo/blob/HEAD/get_primals.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"807ed19e0967f423"}},{"code_sha256_prefix":"8069ebf3e5c1a826","entry":"reorderSMSP","repo":"netsysopt/symilo","repo_kind":"official","path":"utils.py","file_url":"https://github.com/netsysopt/symilo/blob/HEAD/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"8069ebf3e5c1a826"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}