arXiv:2607.06532v1 Announce Type: new Abstract: Mixed-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
The increasing complexity and application-specific nature of AI models, particularly in areas like Mixed-Integer Linear Programming, necessitates new approaches for generating robust training data and testing environments for solvers and learned policies.
Improved MILP instance generation can significantly accelerate AI solver development, leading to more efficient optimization solutions across various industries and potentially bolstering the capabilities of AI agents.
The introduction of GraphBU proposes a 'graph-native' method for generating MILP instances, explicitly addressing how local parts of the problem are coupled, which could lead to more structurally relevant and challenging test cases for AI models and traditional optimization solvers.
- · AI solver developers
- · Optimization software companies
- · Logistics and supply chain sectors
- · Manufacturing industries
- · Developers relying on generic or unstructured instance generation
More robust and generalizable AI models for complex optimization problems will emerge.
Enhanced capabilities of AI agents to solve real-world scheduling, resource allocation, and design tasks.
Accelerated deployment of advanced automation and decision-making systems across critical infrastructure and operations.
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Read at arXiv cs.LG