Linear Operators and related optimization algorithms
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README.rst

linear_operators

What is linear_operators ?

linear_operators is a package implementing estimation algorithms for large scale linear problems. The workflow is as follows :

  • generate a linear model using LinearOperator instances as buildling blocks,
  • define a criterion to minimize using this model (e.g. least-square),
  • finally, perform minimization on the criterion using a minimization algorithm (e.g. conjugate gradient).

Subpackages implements each part of this workflow. Here is a list of the subpackages :

  • interface : Taken from the scipy.sparse package. It implement the LinearOperator class which replaces matrices and do not require to store any matrix coefficient. It makes use of matrix-vector operations instead.
  • operators : A set of LinearOperator subclasses implementing various linear operation on vectors and their transpose.
  • ndoperators : A set of LinarOperator subclasses implementing operations on multidimensional arrays.
  • iterative : Contains the Criterion class which allows to define objective function as well as minimizers such as the conjugate gradient algorithms and wrappers to other minimizing algorithms.
  • wrappers : define extra LinearOperator subclasses if optional dependencies are available.

Requirements

List of requirements:

  • numpy >= 1.3
  • scipy >= 0.8

Optional requirements:

  • PyWavelets
  • fht (fast hadamard transform)