Cuopt Debugging

cuOpt Debugging Skill

Diagnose and fix issues with cuOpt LP/MILP solutions, errors, and performance.

Before You Start: Required Questions

Ask these to understand the problem:

• What's the symptom?
• Error message?
• Wrong/unexpected results?
• Empty solution?
• Performance too slow?

• What's the status?
• problem.Status.name — what value does it show?

• Can you share?
• The error message (exact text)
• The code that produces it
• Problem size (variables, constraints)

Quick Diagnosis by Symptom

"Solution is empty/None but status looks OK"

Most common cause: Wrong status string case

# ❌ WRONG - "OPTIMAL" never matches, silently fails
if problem.Status.name == "OPTIMAL":
    print(problem.ObjValue)  # Never runs!

# ✅ CORRECT - use PascalCase
if problem.Status.name in ["Optimal", "FeasibleFound"]:
    print(problem.ObjValue)

Diagnostic code:

print(f"Actual status: '{problem.Status.name}'")
print(f"Matches 'Optimal': {problem.Status.name == 'Optimal'}")
print(f"Matches 'OPTIMAL': {problem.Status.name == 'OPTIMAL'}")

"Objective value is wrong/zero"

Check if variables are actually used:

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()}")

Common causes:

• Constraints too restrictive (all zeros is feasible)
• Objective coefficients have wrong sign
• Wrong variable in objective

"Infeasible" status

For LP/MILP:

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}")

Common causes:

• Conflicting constraints (x <= 5 AND x >= 10)
• Bounds too tight
• Missing a "slack" variable for soft constraints

"Integer variable has fractional value"

# 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")

"Unbounded" status

Problem has no finite optimum:

if problem.Status.name in ["DualInfeasible", "Unbounded"]:
    print("Problem is unbounded - objective can improve infinitely")

Common causes:

• Missing variable upper/lower bounds
• Constraint direction wrong (>= instead of <=)
• Missing constraints

"Maximum recursion depth exceeded" when building expressions

Building large objectives or constraints with many chained + operations can hit Python recursion limits. Use LinearExpression instead:

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)

See the LP/MILP "Building large expressions" section and reference models in the project for examples.

OutOfMemoryError

Check problem size:

print(f"Variables: {len(problem.getVariables())}")
print(f"Constraints: {len(problem.getConstraints())}")

Mitigations:

• Reduce problem size
• Use sparse constraint matrix
• Set time limit to get partial solution

Status Code Reference

LP Status Values

| 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 |

MILP Status Values

| 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 |

Performance Debugging

Slow LP/MILP Solve

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% gap

Check Solve Time

problem.solve(settings)
print(f"Solve time: {problem.SolveTime:.2f} seconds")

Diagnostic Checklist

□ 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?

Diagnostic Code Snippets

See resources/diagnostic_snippets.md for copy-paste diagnostic code:

• Status checking
• Variable inspection
• Constraint analysis
• Memory and performance checks

Interpreting Dual Values & Reduced Costs

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.)

When to Escalate

File a GitHub issue if:

• Reproducible bug with minimal example
• Include: cuOpt version, CUDA version, error message, minimal repro code