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Matrix data structures, linear solvers, least squares methods, eigenvalue, and singular value decompositions. For larger random dense matrices (above ~ 350 x 350) matrix-matrix multiplication C = A.B is about 50% faster than MTJ.

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Unstructured sparse matrices and vectors with iterative solvers and
preconditioners. The classes and interfaces can be grouped as follows:

 General sparse matrices
  
   CompRowMatrix -
    Compressed row storage. Generally the best sparse matrix if the non-zero
    structure is known.
   CompColMatrix -
    Compressed column storage.
   CompDiagMatrix -
    Compressed diagonal storage.
   FlexCompRowMatrix -
    Flexible compressed row storage. Stores each row as a growable sparse
    vector.
   FlexCompColMatrix -
    Flexible compressed column storage. Stores each column as a growable sparse
    vector.
   SparseVector -
    Growable sparse vector.
  
 
 Iterative solvers
  
   BiCG -
   BiConjugate gradients.
   BiCGstab -
   BiConjugate gradients stabilized.
   CG -
   Conjugate gradients.
   CGS -
   Conjugate gradients squared.
   Chebyshev -
   The Chebyshev iteration for symmetrical, positive definite matrices.
   GMRES -
   Generalized minimal residual using restart.
   IR -
   Iterative refinement (Richardson's method).
   QMR -
   Quasi-minimal residual.
  
 
 Preconditioners
  
   DiagonalPreconditioner -
    Diagonal preconditioning.
   SSOR -
    Symmetrical sucessive overrelaxation.
   ICC -
    Incomplete Cholesky without fill-in.
   ILU -
    Incomplete LU without fill-in.
   ILUT -
    Incomplete LU with fill-in using a threshold approach.
   AMG -
    Algebraic multigrid by smoothed aggregation.