sktopt.core.optimizers.oc

class sktopt.core.optimizers.oc.OC_Config(dst_path: str = './result/pytests', interpolation: Literal['SIMP'] = 'SIMP', record_times: int = 20, max_iters: int = 200, p_init: float = 1.0, p: float = 3.0, p_step: int = -1, vol_frac_init: float = 0.8, vol_frac: float = 0.4, vol_frac_step: int = -3, beta_init: float = 1.0, beta: float = 2, beta_step: int = -1, beta_curvature: float = 2.0, beta_eta: float = 0.5, eta: float = 0.5, percentile_init: float = 60, percentile: float = -90, percentile_step: int = -1, filter_radius_init: float = 2.0, filter_radius: float = 1.2, filter_radius_step: int = -3, E0: float = 210000000000.0, E_min: float = 210000000.0, rho_min: float = 0.01, rho_max: float = 1.0, move_limit_init: float = 0.3, move_limit: float = 0.1, move_limit_step: int = -3, restart: bool = False, restart_from: int = -1, export_img: bool = False, export_img_opaque: bool = False, design_dirichlet: bool = False, lambda_lower: float = 1e-07, lambda_upper: float = 10000000.0, sensitivity_filter: bool = False, solver_option: Literal['spsolve', 'cg_pyamg'] = 'spsolve', scaling: bool = False, eta_init: float = 0.1, eta_step: int = 3)

Bases: DensityMethodConfig

eta: float = 0.5
eta_init: float = 0.1
eta_step: int = 3
interpolation: Literal['SIMP'] = 'SIMP'
class sktopt.core.optimizers.oc.OC_Optimizer(cfg: OC_Config, tsk: TaskConfig)

Bases: DensityMethod

Topology optimization solver using the classic Optimality Criteria (OC) method. This class implements the standard OC algorithm for compliance minimization problems. It uses a multiplicative density update formula derived from Karush-Kuhn-Tucker (KKT) optimality conditions under volume constraints.

The update rule typically takes the form:

ρ_new = clamp(ρ * sqrt(-dC / λ), ρ_min, ρ_max)

where:

  • dC is the sensitivity of the compliance objective,

  • λ is a Lagrange multiplier for the volume constraint.

This method is widely used in structural optimization due to its simplicity, interpretability, and solid theoretical foundation.

Advantages

  • Simple and easy to implement

  • Intuitive update rule based on physical insight

  • Well-established and widely validated in literature

config

Configuration object specifying the interpolation method, volume fraction, continuation settings, filter radius, and other numerical parameters.

Type:

DensityMethodConfig

mesh, basis, etc.

FEM components required for simulation, including boundary conditions and loads.

Type:

inherited from common.DensityMethod

init_schedulers(export: bool = True)
rho_update(iter_loop: int, rho_candidate: ndarray, rho_projected: ndarray, dC_drho_ave: ndarray, u_dofs: ndarray, strain_energy_ave: ndarray, scaling_rate: ndarray, move_limit: float, eta: float, beta: float, tmp_lower: ndarray, tmp_upper: ndarray, percentile: float, elements_volume_design: ndarray, elements_volume_design_sum: float, vol_frac: float)
sktopt.core.optimizers.oc.bisection_with_projection(dC, rho_e, rho_min, rho_max, move_limit, eta, eps, vol_frac, beta, beta_eta, scaling_rate, rho_candidate, tmp_lower, tmp_upper, elements_volume, elements_volume_sum, max_iter: int = 100, tolerance: float = 0.0001, l1: float = 1e-07, l2: float = 10000000.0)