cuopt-user-rules

Formulating a cuOpt routing problem requires specifying your language interface, exact problem definition, available data, and constraints before generating code. You must provide precise performance targets and validate inputs to ensure safe, reproducible deployments.

Before writing cuOpt LP or MILP configurations, you need exact problem definitions, structured data, and explicit constraints. Specifying your target interface—Python, C, CLI, or REST—and validation steps ensures the solver receives verifiable inputs.

Yes, cuOpt supports QP problems across Python, C, CLI, and REST interfaces. Users must clarify their problem type, data availability, and performance targets to generate safe, verifiable configurations for their chosen environment.

cuOpt deployments fail without clear constraints because the system requires exact problem definitions and validated data to prevent guessing. Providing complete inputs, performance targets, and explicit constraints ensures reproducible and safe optimization results.

The best way to define constraints for cuOpt server deployment is to ask clarifying questions about your data and problem type first. You must specify exact requirements, validate inputs, and outline prerequisites to avoid pitfalls during configuration.

No, cuOpt requires exact data and problem definitions before generating routing configurations. Guessing in problem formulation is prevented by enforcing safe workflows, meaning you must validate input quality and specify all constraints beforehand.