finite_differences module¶

Gradient approximation by finite differences.

class gemseo.utils.derivatives.finite_differences.FirstOrderFD(f_pointer, step=1e-06, parallel=False, design_space=None, normalize=True, **parallel_args)[source]¶

Bases: GradientApproximator

First-order finite differences approximator.

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rac{df(x)}{dx}pprox rac{f(x+delta x)-f(x)}{delta x}

Parameters:

f_pointer (Callable[[ndarray], ndarray]) – The pointer to the function to derive.
step (float | ndarray) –
The default differentiation step.

By default it is set to 1e-06.
parallel (bool) –
Whether to differentiate the function in parallel.

By default it is set to False.
design_space (DesignSpace | None) – The design space containing the upper bounds of the input variables. If None, consider that the input variables are unbounded.
normalize (bool) –
If True, then the functions are normalized.

By default it is set to True.
**parallel_args (int | bool | float) – The parallel execution options, see gemseo.core.parallel_execution.

compute_optimal_step(x_vect, numerical_error=2.220446049250313e-16, **kwargs)[source]¶

Compute the gradient by real step.

Parameters:

x_vect (ndarray) – The input vector.
numerical_error (float) –
The numerical error associated to the calculation of \(f\). By default machine epsilon (appx 1e-16), but can be higher. when the calculation of \(f\) requires a numerical resolution.

By default it is set to 2.220446049250313e-16.
**kwargs – The additional arguments passed to the function.

Returns:

The optimal steps. The errors.

Return type:

tuple[numpy.ndarray, numpy.ndarray]

f_gradient(x_vect, step=None, x_indices=None, **kwargs)[source]¶

Approximate the gradient of the function for a given input vector.

Parameters:

x_vect (ndarray) – The input vector.
step (float | ndarray | None) – The differentiation step. If None, use the default differentiation step.
x_indices (Sequence[int] | None) – The components of the input vector to be used for the differentiation. If None, use all the components.
**kwargs (Any) – The optional arguments for the function.

Returns:

The approximated gradient.

Return type:

ndarray

generate_perturbations(n_dim, x_vect, x_indices=None, step=None)¶

Generate the input perturbations from the differentiation step.

These perturbations will be used to compute the output ones.

Parameters:

n_dim (int) – The input dimension.
x_vect (ndarray) – The input vector.
x_indices (Sequence[int] | None) – The components of the input vector to be used for the differentiation. If None, use all the components.
step (float | None) – The differentiation step. If None, use the default differentiation step.

Returns:

The input perturbations.
The differentiation step, either one global step or one step by input component.

Return type:

tuple[ndarray, float | ndarray]

ALIAS = 'finite_differences'¶

f_pointer: Callable[[ndarray], ndarray]¶: The pointer to the function to derive.

property step: float¶: The default approximation step.

gemseo.utils.derivatives.finite_differences.approx_hess(f_p, f_x, f_m, step)[source]¶

Compute the second-order approximation of the Hessian matrix \(d^2f/dx^2\).

Parameters:

f_p (ndarray) – The value of the function \(f\) at the next step \(x+\\delta_x\).
f_x (ndarray) – The value of the function \(f\) at the current step \(x\).
f_m (ndarray) – The value of the function \(f\) at the previous step \(x-\\delta_x\).
step (float) – The differentiation step \(\\delta_x\).

Returns:

The approximation of the Hessian matrix at the current step \(x\).

Return type:

ndarray

gemseo.utils.derivatives.finite_differences.comp_best_step(f_p, f_x, f_m, step, epsilon_mach=2.220446049250313e-16)[source]¶

Compute the optimal step for finite differentiation.

Applied to a forward first order finite differences gradient approximation.

Require a first evaluation of the perturbed functions values.

The optimal step is reached when the truncation error (cut in the Taylor development), and the numerical cancellation errors (round-off when doing \(f(x+step)-f(x))\) are equal.