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SKILL.md
npx skills add nvidia/cuopt-examples --skill cuopt-debugging
Troubleshoot cuOpt LP/MILP problems including errors, wrong results, infeasible solutions, performance issues, and status codes. Use when the user says something isn't working, gets unexpected results, or needs help diagnosing issues.
npx skills add nvidia/cuopt-examples --skill cuopt-debugging
Diagnose and fix issues with cuOpt LP/MILP solutions, errors, and performance.
Ask these to understand the problem:
What's the symptom?
What's the status?
problem.Status.name — what value does it show?Can you share?
Most common cause: Wrong status string case
Diagnostic code:
Check if variables are actually used:
Common causes:
For LP/MILP:
Common causes:
Problem has no finite optimum:
Common causes:
Building large objectives or constraints with many chained + operations can hit Python recursion limits. Use LinearExpression instead:
See the LP/MILP "Building large expressions" section and reference models in the project for examples.
Check problem size:
Mitigations:
| Status | Meaning |
|---|---|
Optimal | Found optimal solution |
PrimalFeasible | Found feasible but may not be optimal |
PrimalInfeasible | No feasible solution exists |
DualInfeasible | Problem is unbounded |
TimeLimit | Stopped due to time limit |
IterationLimit | Stopped due to iteration limit |
NumericalError | Numerical issues encountered |
NoTermination | Solver didn't converge |
| Status | Meaning |
|---|---|
Optimal | Found optimal solution |
FeasibleFound | Found feasible, within gap tolerance |
Infeasible | No feasible solution exists |
Unbounded | Problem is unbounded |
TimeLimit | Stopped due to time limit |
NoTermination | No solution found yet |
See resources/diagnostic_snippets.md for copy-paste diagnostic code:
When an LP/QP solve returns dual values and you need the decision read — which constraint is the binding bottleneck, what relaxing it is worth, and which unused option is the closest near-miss — see resources/interpreting_duals.md. (Integer models / MILP — and quadratic constraints — return no usable duals; that reference covers the fallback.)
File a GitHub issue if:
print(f"Actual status: '{problem.Status.name}'")
print(f"Matches 'Optimal': {problem.Status.name == 'Optimal'}")
print(f"Matches 'OPTIMAL': {problem.Status.name == 'OPTIMAL'}")for var in problem.getVariables():
print(f"{var.VariableName} = {var.Value}")
print(f"Objective: {problem.ObjValue}")
# Or with direct variable references
for var in [x, y, z]:
print(f"{var.VariableName}: {var.getValue()}")if problem.Status.name in ["PrimalInfeasible", "Infeasible"]:
print("Problem has no feasible solution")
# Review constraints for conflicts
for c in problem.getConstraints():
print(f"{c.ConstraintName}")# Check how variable was defined
int_var = problem.addVariable(
lb=0, ub=10,
vtype=INTEGER, # Must be INTEGER, not CONTINUOUS
name="count"
)
# Also check if status is actually optimal
if problem.Status.name == "FeasibleFound":
print("Warning: not fully optimal, may have fractional intermediate values")if problem.Status.name in ["DualInfeasible", "Unbounded"]:
print("Problem is unbounded - objective can improve infinitely")from cuopt.linear_programming.problem import LinearExpression
# Instead of: expr = c1*v1 + c2*v2 + ... + cn*vn (many terms)
vars_list = [v1, v2, v3, ...]
coeffs_list = [c1, c2, c3, ...]
expr = LinearExpression(vars_list, coeffs_list, constant=0.0)
problem.setObjective(expr, sense=MINIMIZE)print(f"Variables: {len(problem.getVariables())}")
print(f"Constraints: {len(problem.getConstraints())}")settings = SolverSettings()
settings.set_parameter("log_to_console", 1) # See progress
settings.set_parameter("time_limit", 60) # Don't wait forever
# For MILP, accept good-enough solution
settings.set_parameter("mip_relative_gap", 0.05) # 5% gapproblem.solve(settings)
print(f"Solve time: {problem.SolveTime:.2f} seconds")□ Status checked with correct case (PascalCase)?
□ All variables have correct vtype (INTEGER vs CONTINUOUS)?
□ Constraint directions correct (<= vs >= vs ==)?
□ Objective sense correct (MINIMIZE vs MAXIMIZE)?
□ Variable bounds specified where needed?