CP Solvers — Google OR-Tools CP-SAT

This page covers constraint-programming (CP) backends, which are neither direct QUBO/HUBO samplers (see QUBO/HUBO Solvers) nor MILP solvers (see MILP Solvers). A CP engine searches over Boolean / integer variables under constraints with a SAT-style core and returns a proven optimum.

⚠️ Experimental — PyQBPP only. The OR-Tools library itself is a production tool; the PyQBPP integration is experimental and its wrapper API may change without notice. OR-Tools ships only as a Python package (pip install ortools), so this solver has no C++ entry point. It is imported lazily when the solver is instantiated.

OrToolsCpSatSolver

Google OR-Tools CP-SAT — a constraint-programming engine with a SAT-solver core. CP-SAT doesn’t natively accept quadratic objectives; PyQBPP encodes each non-linear monomial ℓ_a ℓ_b ... ℓ_k (each ℓ either x_i or ~x_i) as a fresh Boolean z with z = ℓ_a ∧ ... ∧ ℓ_k and minimizes the resulting linear objective. HUBO of any degree works with the same encoding, and negated literals are handled natively via CP-SAT’s BoolVar.Not() — no all_positive expansion is applied (which would multiply each m-negation monomial into 2^m sub-terms)::

sol = qbpp.OrToolsCpSatSolver(e).search(time_limit=5.0)

This makes OrToolsCpSatSolver the only external solver in PyQBPP that accepts arbitrary-degree HUBO and negated literals natively while returning a proven optimum.

Common search() kwargs: time_limit (s, mapped to parameters.max_time_in_seconds), thread_count (num_search_workers), log (boolean).

Common return type

OrToolsCpSatSolver returns the standard PyQBPP SolverSol (same as EasySolverSol/ABS3SolverSol), so the rest of your program is solver-agnostic:

print(sol.energy)            # best objective value
print(sol.tts)               # time-to-best-solution (seconds)
print(sol.info["solver"])    # "OrToolsCpSatSolver"
for s in sol.sols:           # additional candidate solutions
    print(s.energy, s.tts)