2021/07/15 by Mohit Kumar, Samuel Kolb, Kumar, Mohit +5
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Constraint Satisfaction and Optimization #FOS: Computer and information sciences #Machine Learning and Algorithms #Scheduling and Timetabling Solutions #cs.AI
paper · pdf · doi:10.48550/arxiv.2107.07136
arxiv created 2021/07/15 · openalex publication_date 2021/07/15 · arxiv updated 2021/07/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Mixed-integer linear programs (MILPs) are widely used in artificial intelligence and operations research to model complex decision problems like scheduling and routing. Designing such programs however requires both domain and modelling expertise. In this paper, we study the problem of acquiring MILPs from contextual examples, a novel and realistic setting in which examples capture solutions and non-solutions within a specific context. The resulting learning problem involves acquiring continuous parameters -- namely, a cost vector and a feasibility polytope -- but has a distinctly combinatorial flavor. To solve this complex problem, we also contribute MISSLE, an algorithm for learning MILPs from contextual examples. MISSLE uses a variant of stochastic local search that is guided by the gradient of a continuous surrogate loss function. Our empirical evaluation on synthetic data shows that MISSLE acquires better MILPs faster than alternatives based on stochastic local search and gradient descent.