class pylops.SecondDerivative(N, dims=None, dir=0, sampling=1, edge=False, dtype='float64')[source]

Second derivative.

Apply second-order second derivative.

N : int

Number of samples in model.

dims : tuple, optional

Number of samples for each dimension (None if only one dimension is available)

dir : int, optional

Direction along which smoothing is applied.

sampling : float, optional

Sampling step dx.

edge : bool, optional

Use reduced order derivative at edges (True) or ignore them (False)

dtype : str, optional

Type of elements in input array.


The SecondDerivative operator applies a second derivative to any chosen direction of a multi-dimensional array.

For simplicity, given a one dimensional array, the second-order centered first derivative is:

\[y[i] = (x[i+1] - 2x[i] + x[i-1]) / dx^2\]
shape : tuple

Operator shape

explicit : bool

Operator contains a matrix that can be solved explicitly (True) or not (False)


__init__(self, N[, dims, dir, sampling, …]) Initialize this LinearOperator.
adjoint(self) Hermitian adjoint.
cond(self, \*\*kwargs_eig) Condition number of linear operator.
conj(self) Complex conjugate operator
div(self, y[, niter]) Solve the linear problem \(\mathbf{y}=\mathbf{A}\mathbf{x}\).
dot(self, x) Matrix-matrix or matrix-vector multiplication.
eigs(self[, neigs, symmetric, niter]) Most significant eigenvalues of linear operator.
matmat(self, X) Matrix-matrix multiplication.
matvec(self, x) Matrix-vector multiplication.
rmatvec(self, x) Adjoint matrix-vector multiplication.
transpose(self) Transpose this linear operator.