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: gemseo.utils.derivatives.gradient_approximator.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.

By default it is set to None.
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.

Return type

None

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

Compute the gradient by real step.

Parameters

x_vect (numpy.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.

By default it is set to None.
x_indices (Sequence[int] | None) –
The components of the input vector to be used for the differentiation. If None, use all the components.

By default it is set to None.
**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.

By default it is set to None.
step (float | None) –
The differentiation step. If None, use the default differentiation step.

By default it is set to None.

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[[numpy.ndarray], numpy.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 (numpy.ndarray) – The value of the function \(f\) at the next step \(x+\\delta_x\).
f_x (numpy.ndarray) – The value of the function \(f\) at the current step \(x\).
f_m (numpy.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

numpy.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.