Optimization
Optiland supports optimization through different backends. The NumPy backend relies on SciPy optimizers, while the Torch backend uses PyTorch’s native optimization tools.
NumPy (SciPy) Optimization
These examples show how Optiland integrates with SciPy’s optimizers to improve optical systems using classical numerical methods.
RMS Spot Size Optimization
Wavefront Error Optimization
Undoing Optimization
Pickups
Constrained Optimization
Bounded Optimization Operands
Orthogonal Descent Optimization
Global Optimization (Differential Evolution)
Global Optimization (Basin Hopping)
Global Optimization (SHGO)
Custom Variable Scaling
Torch Optimization
These examples demonstrate optimization using the Torch backend, taking advantage of PyTorch’s autograd and optimizers for differentiable design.
