Optimization Problems

Optimization problems expose typed bounds, solver-independent constraints, and problem-specific representative baseline solve() implementations.

The smooth continuous packaged benchmarks in this family continue to use SciPy baselines. If you install design-research-problems[solvers], the open-ended battery co-design optimizer will automatically prefer a pymoo genetic search, then a nevergrad derivative-free search, and otherwise fall back to the built-in deterministic local search.

See Optimization Problem Catalog for the generated per-problem catalog pages.

The packaged entries include:

  • battery_pack_18650_open_ended_capacity_max, an explicit 18650 transition-program co-design problem that maximizes delivered capacity under the shared battery backend.

  • 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.

  • battery_pack_18650_series_parallel_cost_min, a fixed-topology integer sizing problem over canonical rectangular 18650 battery packs that reuses the shared battery backend.

  • 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.