5.1. Numdifftools summary¶
5.1.1. numdifftools.core module¶
Derivative(fun[, step, method, order, n, …]) |
Calculate n-th derivative with finite difference approximation |
Gradient(fun[, step, method, order, n, …]) |
Calculate Gradient with finite difference approximation |
Jacobian(fun[, step, method, order, n, …]) |
Calculate Jacobian with finite difference approximation |
Hessdiag(f[, step, method, order, full_output]) |
Calculate Hessian diagonal with finite difference approximation |
Hessian(f[, step, method, order, full_output]) |
Calculate Hessian with finite difference approximation |
directionaldiff(f, x0, vec, \*\*options) |
Return directional derivative of a function of n variables |
5.1.2. Step generators¶
BasicMaxStepGenerator(base_step, step_ratio, …) |
Generates a sequence of steps of decreasing magnitude |
BasicMinStepGenerator(base_step, step_ratio, …) |
Generates a sequence of steps of decreasing magnitude |
MinStepGenerator([base_step, step_ratio, …]) |
Generates a sequence of steps |
MaxStepGenerator([base_step, step_ratio, …]) |
Generates a sequence of steps |
5.1.3. numdifftools.extrapolation module¶
convolve(sequence, rule, \*\*kwds) |
Wrapper around scipy.ndimage.convolve1d that allows complex input. |
Dea([limexp]) |
Extrapolate a slowly convergent sequence |
dea3(v0, v1, v2[, symmetric]) |
Extrapolate a slowly convergent sequence |
Richardson([step_ratio, step, order, num_terms]) |
Extrapolates as sequence with Richardsons method |
5.1.4. numdifftools.limits module¶
CStepGenerator([base_step, step_ratio, …]) |
Generates a sequence of steps |
Limit(fun[, step, method, order, full_output]) |
Compute limit of a function at a given point |
Residue(f[, step, method, order, …]) |
Compute residue of a function at a given point |
5.1.5. numdifftools.multicomplex module¶
Bicomplex(z1, z2) |
Creates an instance of a Bicomplex object. |
5.1.6. numdifftools.nd_algopy module¶
Derivative(fun[, n, method, full_output]) |
Calculate n-th derivative with Algorithmic Differentiation method |
Gradient(fun[, n, method, full_output]) |
Calculate Gradient with Algorithmic Differentiation method |
Jacobian(fun[, n, method, full_output]) |
Calculate Jacobian with Algorithmic Differentiation method |
Hessdiag(f[, method, full_output]) |
Calculate Hessian diagonal with Algorithmic Differentiation method |
Hessian(f[, method, full_output]) |
Calculate Hessian with Algorithmic Differentiation method |
directionaldiff(f, x0, vec, \*\*options) |
Return directional derivative of a function of n variables |