Optimization Problems#

Optimization problems expose typed bounds, solver-independent constraints, and problem-specific representative baseline solve() implementations. They also expose solver_hints() for agent-facing solver selection. The hint payload includes variable domain, numeric bounds, constraint counts, objective mode, and a compact solver-family recommendation, and the same payload is available through the solver_hints tool when an optimization problem is served over MCP.

The smooth continuous packaged benchmarks in this family continue to use SciPy baselines.

See Optimization Problem Catalog for the generated per-problem catalog pages. For deeper battery-model details and equations, see Battery Ladder Technical Notes.

The packaged entries include:

  • The canonical battery pack compatibility matrix and the meaning of native, projected, and promoted live in Battery Ladder Technical Notes.

  • battery_18650_t1_rectangular_surrogate_opt, a tier-1 rectangular sizing benchmark with a fixed rectangular series-parallel representation and analytic_surrogate, explicit_circuit, or hybrid_thermal scoring.

  • battery_18650_t2_pose_surrogate_opt, a tier-2 pose-aware layout benchmark that adds per-cell [x_mm, y_mm, z_mm, angle_x_deg, angle_y_deg, angle_z_deg] freedom while supporting analytic_surrogate, promoted explicit_circuit, and promoted hybrid_thermal evaluation.

  • battery_18650_t3a_topology_explicit_2rc_opt, a manifest-backed tier-3A backend-config example that defaults to projected explicit_circuit evaluation with the shared pybamm_ecm_2rc battery backend.

  • battery_18650_t3a_topology_surrogate_opt, a tier-3A topology-allocation benchmark that adds active-cell count plus stage assignment and supports analytic_surrogate, projected explicit_circuit, and promoted hybrid_thermal evaluation modes.

  • battery_18650_t3b_netlist_explicit_opt, a tier-3B explicit-netlist benchmark that exposes open-ended transition-program design variables and supports native explicit_circuit and promoted hybrid_thermal scoring.

  • battery_18650_t4_thermal_hybrid_2rc_opt, a manifest-backed tier-4 backend-config example that keeps hybrid_thermal scoring while selecting the shared pybamm_ecm_2rc battery backend.

  • battery_18650_t4_thermal_hybrid_opt, a tier-4 thermal-topology benchmark that adds cooling-system variables and supports analytic_surrogate, explicit_circuit, and hybrid_thermal evaluator modes.

  • battery_fast_charge_dfn_anchor_opt, a PyBaMM DFN-based electrochemical anchor benchmark whose packaged solve() method is a deterministic baseline/reference search rather than a claim of strong optimization.

  • gmpb_default_dynamic_min, a stateful dynamic wrapper that negates the native Generalized Moving Peaks Benchmark maximization score to fit this package’s minimization-oriented optimization API.

  • planar_truss_span_mass_min, a fixed-joint binary planar-truss problem that minimizes structural mass under hard factor-of-safety and deflection limits.

  • planar_truss_span_deflection_min, a fixed-joint binary planar-truss problem that minimizes structural deflection under hard factor-of-safety and mass limits.

  • planar_truss_span_fos_max, a fixed-joint binary planar-truss problem that maximizes factor of safety under hard mass and deflection limits.

  • space_truss_span_mass_min, a fixed-joint binary 3D space-truss problem that minimizes structural mass under hard factor-of-safety and deflection limits.

  • pill_capsule_min_area, a compact nonlinear constrained problem with two continuous variables.

  • moneymaker_hip_pump_cost_min, a citation-backed scalarized cost minimization benchmark derived from the MoneyMaker Hip Pump studies.

  • treadle_pump_ide_material_min, a citation-backed scalarized material minimization benchmark derived from the IDE-style treadle pump studies.