Coordinatebased Dimension Reduction¶
The goal of a coordinate based dimension reduction is to identify subsets of variables that are sufficient to explain the behavor of the function; this process is sometimes known as variable screening.
Although in many ways this is a simpler process than subspace based dimension reduction, from the point of view of the code, a coordinatebased dimension reduction is a special case of a subspacebased dimension reduction where subspaces are built from columns of the identity matrix.

class
psdr.
CoordinateBasedDimensionReduction
[source]¶ Abstract base class for dimension reduction strategies that select variables

U
¶ A matrix defining the ‘important’ directions
Returns: Matrix with orthonormal columns defining the important directions in decreasing order of precidence. Return type: np.ndarray (m, n)

score
¶ The score associated with each parameter

Diagonal Lipschitz Matrix¶

class
psdr.
DiagonalLipschitzMatrix
(epsilon=None, method='cvxopt', **kwargs)[source]¶ Constructs a diagonal Lipschitz matrix
Much like the standard Lipschitz matrix class
psdr.LipschitzMatrix()
, this class computes a Lipschitz matrix except with the constraint the matrix is diagonal.