gemseo / core / derivatives

derivation_modes module

Derivation modes for the GEMSEO processes.

gemseo.core.derivatives.derivation_modes.ADJOINT_MODE = 'adjoint'

The adjoint resolution mode for MDAs, solves one system per output.

gemseo.core.derivatives.derivation_modes.AUTO_MODE = 'auto'

Automatic switch between direct, reverse or adjoint depending on data sizes.

gemseo.core.derivatives.derivation_modes.AVAILABLE_APPROX_MODES = ('complex_step', 'finite_differences')

The approximation derivation modes.

gemseo.core.derivatives.derivation_modes.AVAILABLE_MODES = ('direct', 'adjoint', 'auto', 'reverse')

All possible derivation modes

gemseo.core.derivatives.derivation_modes.COMPLEX_STEP = 'complex_step'

The complex step method used to approximate the Jacobians by perturbing each variable with a small complex number.

gemseo.core.derivatives.derivation_modes.DIRECT_MODE = 'direct'

The direct Jacobian accumulation, chain rule from inputs to outputs, or derivation of an MDA that solves one system per input.

gemseo.core.derivatives.derivation_modes.FINITE_DIFFERENCES = 'finite_differences'

The finite differences method used to approximate the Jacobians by perturbing each variable with a small real number.

gemseo.core.derivatives.derivation_modes.REVERSE_MODE = 'reverse'

The reverse Jacobian accumulation, chain rule from outputs to inputs.