[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83113-en":3,"doc-seo-83113-105":29,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},83113,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","GraphBU: MILP Instance Generation with Graph-Native Block Units","Mixed-integer linear programming (MILP) instances used for solver development are difficult to obtain when models originate from private or application-specific pipelines. GraphBU introduces a graph-native generator with a basic unit consisting of a local subproblem plus an explicit interface, ensuring recorded coupling between parts of the MILP. Interface promotion builds master constraints or boundary variables, enabling compatibility-checked replacement. Construction analysis covers interface separation, interface-slack feasibility preservation, and invariance to row-column permutations, yielding high similarity, feasibility, and improved Predict-and-Search performance.","GraphBU: MILP Instance Generation with Graph-Native Block Units  \nXiaolei Guo 1 , Chenyu Zhou 1 , Jianghao Lin 1 ∗ , Dongdong Ge 1  \n1 Shanghai Jiao Tong University  \n{lionelgxl, chenyuzhou, linjianghao, [ddge}@sjtu.edu.cn](ddge}@sjtu.edu.cn)  \narXiv :2607 .06532v 1 [ cs .LG] 7 Jul 2026  \nAbstract  \nMixed-integer linear programming (MILP) instances used for solver development are hard to obtain when models come from private or application-specific pipelines. A generator must keep the structure that solvers and learned policies rely on. Existing general generators usually choose their generation unit from a formulation template, summary statistics, local graph edits, or blocks found after recombination. These units do not explicitly record how a local part of the MILP is coupled to the rest of the instance. We propose GraphBU, a graph-native generator whose basic unit is a local subproblem plus its interface. The method promotes coupling nodes into master constraints or boundary variables and uses the resulting block units for compatibility-checked replacement. The analysis focuses on the properties needed by this construction:  \npromotion separates interfaces, replacement can preserve feasibility under an interface-slack condition, and the graph construction is invariant to row-column permutations. On MILP instances generation, this unit keeps graph statistics close to the source family, preserves feasibility on most datasets, and improves downstream Predict-and-Search training. Genrated by GraphBU, The average graph-statistical similarity was approximately 0.934, the average feasibility was approximately 96.7%, and the average increase in the main index of downstream PS was approximately 8.0% .  \nIntroduction  \nMixed-integer linear programming (MILP) is a standard way to model decisions that mix discrete choices with linear constraints, and it appears in applications such as scheduling, planning, logistics, and chip design. In deployed optimization systems, even small changes in solve time can affect throughput, resource utilization, or service quality. Solver development therefore depends on representative instance data: classical solvers use it for parameter tuning and stress testing, while learning-based solvers require it for training(Bengio, Lodi, and Prouvost 2021; Khalil et al. 2016; Nair et al. 2020; Han et al. 2023) . Benchmarks also need various instances to expose failures that a small test set would miss. The difficulty is that real MILP instances are often expensive to collect, tied to private business data, or generated inside closed modeling  \n∗Corresponding author.  \nCopyright © 2027, Association for the Advancement of Artificial Intelligence ([www.aaai.org](www.aaai.org)). All rights reserved.  \nFigure 1: Generation-unit mismatch in MILP instance generation. GraphBU uses graph-native block units with explicit interfaces.  \npipelines. This motivates the task of MILP instance generation, which aims to produce additional instances that behave like a target family without requiring access to its original modeling pipeline.  \nA generated MILP is useful only if it remains faithful to the target instance family. Feasibility alone is only a minimal requirement. The generated instances should preserve the  \nscale, sparsity, coefficient patterns, feasibility behavior, and solving difficulty of the source family. This requirement is especially strict for learning-based solver modules, which learn policies from the graph structure of training instances(Gasseet al. 2019; Prouvost et al. 2020) . If generation changes how constraints and variables are connected, a policy trained on the generated data may not transfer to the original family. The choice of generation unit therefore becomes a design issue: the generator must decide which part of a MILP can be reused without breaking the structure that solvers rely on.  \nExisting generators expose the same issue from different angles. When the formulation is known, new instance","cbCaicCtxuSwHoqO","https://ap.wps.com/l/cbCaicCtxuSwHoqO","pdf",902624,1,12,"English","en",105,"# Abstract\n# Introduction\n# GraphBU Method Overview\n## Unit construction and interface slicing\n# Experiments and Results","[{\"question\":\"What empirical improvements does GraphBU report for generated instances and downstream training?\",\"answer\":\"Across four MILP families, generated instances stay close to the source family in graph-statistical similarity (about 0.934) with high average feasibility (about 96.7%). The generated data also improves downstream Predict-and-Search training, with an average main-index increase of about 8.0%.\"}]",1784185346,30,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":27},"graphbu-milp-instance-generation-with-graph-native-block-units","",{"@graph":35,"@context":77},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/graphbu-milp-instance-generation-with-graph-native-block-units/83113/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"What empirical improvements does GraphBU report for generated instances and downstream training?","Question",{"text":75,"@type":76},"Across four MILP families, generated instances stay close to the source family in graph-statistical similarity (about 0.934) with high average feasibility (about 96.7%). The generated data also improves downstream Predict-and-Search training, with an average main-index increase of about 8.0%.","Answer","https://schema.org",{"og:url":51,"og:type":79,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":81,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,114,119,122,126],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":45,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":45,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":45,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":28,"slug":113},"research-report",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":45,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":98,"slug":129},19,"General","general"]