tensormesh.optimizer

OC Optimizer

class OCOptimizer(params: Tensor | List[Tensor], vf: float, move_limit: float = 0.2, rho_min: float = 1e-3, rho_max: float = 1.0, eta: float = 0.5, bisection_tol: float = 1e-4, bisection_max_iter: int = 50)

基类：object

Optimality Criteria (OC) optimizer for topology optimization.

Similar interface to torch.optim.Optimizer, but specifically designed for density-based topology optimization with volume constraints.

The OC update rule is:

\[\rho_{\text{new}} = \rho \cdot B_e^{\eta}\]

where:

\[B_e = \sqrt{\frac{-\frac{\partial C}{\partial \rho}}{\lambda \cdot \frac{\partial V}{\partial \rho}}}\]

• \(\lambda\): Lagrange multiplier (found by bisection to satisfy volume constraint)

• \(\eta = 0.5\): damping exponent

参数:

• params (Tensor or List[Tensor]) -- Tensor or list of tensors (design variables)

• vf (float) -- Target volume fraction

• move_limit (float, optional) -- Maximum density change per iteration default: 0.2

• rho_min (float, optional) -- Minimum density default: 1e-3

• rho_max (float, optional) -- Maximum density default: 1.0

• eta (float, optional) -- Damping exponent default: 0.5

• bisection_tol (float, optional) -- Tolerance for bisection default: 1e-4

• bisection_max_iter (int, optional) -- Maximum bisection iterations default: 50

示例

optimizer = OCOptimizer(rho, vf=0.5, move_limit=0.2)

for epoch in range(max_iter):
    # Forward pass and compute compliance
    compliance = compute_compliance(rho)

    # Backward pass
    compliance.backward()

    # OC update
    optimizer.step(rho.grad)
    optimizer.zero_grad()

__init__(params: Tensor | List[Tensor], vf: float, move_limit: float = 0.2, rho_min: float = 1e-3, rho_max: float = 1.0, eta: float = 0.5, bisection_tol: float = 1e-4, bisection_max_iter: int = 50)

zero_grad()

Clear gradients of all parameters.

step(dc: Tensor | None = None, dv: Tensor | None = None)

Perform one OC update step.

If dc is omitted, uses each parameter's gradient (negative for compliance minimization). If dv is omitted, uses a uniform volume sensitivity. Returns a dict with keys lambda and volume.

get_volume()

Get current volume fraction.

get_stats()

Get optimizer statistics.