Confirm the objective has squared or cross terms (QP); if purely linear, use LP/MILP. QP must be minimization.
This skill is Python only. QP is beta.
CRITICAL: MINIMIZE only
# ❌ WRONG
problem.setObjective(x*x + y*y, sense=MAXIMIZE)
# ✅ CORRECT — negate for maximization
problem.setObjective(-(x*x + y*y), sense=MINIMIZE)
Portfolio Example
from cuopt.linear_programming.problem import Problem, CONTINUOUS, MINIMIZE
from cuopt.linear_programming.solver_settings import SolverSettings
problem = Problem("Portfolio")
x1 = problem.addVariable(lb=0, ub=1, vtype=CONTINUOUS, name="stock_a")
x2 = problem.addVariable(lb=0, ub=1, vtype=CONTINUOUS, name="stock_b")
x3 = problem.addVariable(lb=0, ub=1, vtype=CONTINUOUS, name="stock_c")
r1, r2, r3 = 0.12, 0.08, 0.05 # expected returns (12%, 8%, 5%)
problem.setObjective(
0.04*x1*x1 + 0.02*x2*x2 + 0.01*x3*x3 + 0.02*x1*x2 + 0.01*x1*x3 + 0.016*x2*x3,
sense=MINIMIZE
)
problem.addConstraint(x1 + x2 + x3 == 1, name="budget")
problem.addConstraint(r1*x1 + r2*x2 + r3*x3 >= 0.08, name="min_return")
problem.solve(SolverSettings())
Status (PascalCase)
if problem.Status.name in ["Optimal", "PrimalFeasible"]:
print(problem.ObjValue)
Debugging
Diagnostic: print(f"Actual status: '{problem.Status.name}'"). For numerical issues, check Q is PSD and variables are scaled.
Examples
- examples.md — portfolio, least squares, maximization workaround
- Reference models: This skill's
assets/— portfolio, least_squares, maximization_workaround. See assets/README.md.
Escalate
If the problem is linear (no squared or cross terms), use LP/MILP. For contribution or build-from-source, see the developer skill.