tree ca524f2664f250e5c994663840d386ea09633601
parent f62dccdb37f2104856c621bce7fd79e7c92d5a9b
author Sameer Agarwal <sameeragarwal@google.com> 1659984169 -0700
committer Sameer Agarwal <sameeragarwal@google.com> 1660051988 +0000

Refactor ConjugateGradientsSolver

1. Convert it from a class to a template function. Where the
   template parameter is "DenseVectorType". This allows us
   to have a single implementation of Conjugate Gradients
   without worrying about where the matrix and the vectors
   are stored or what their internal representation is.

   For the case of CPU based vectors, we abstract operations
   on Eigen vectors using eigen_vector_ops.
2. Introduce ConjugateGradientsLinearOperator which is
   templated on DenseVectorType. It is the matrix vector
   multiplication abstraction.
3. Port the tests and all usages of ConjugateGradientsSolver
   to this new implementation.
4. Introduce Eigen::Vector based RightMultiply and LeftMultiply
   methods into LinearOperator which by default delete to the
   bare pointer based interfaces.
5. Add an identity preconditioner.

These changes are being made in preparation for adding a CUDA
based CGNR solver.

Change-Id: I9da36dc6c131856dd1a4aa7e645aaf12d25dd79b