LAPACK 3.2 is now supported in the latest MKL 10.2 version.
238 new LAPACK functions
Extra Precise Iterative Refinement : New linear solvers that guarantee fully accurate answers (or give a warning that the answer cannot be trusted)
Non-Negative Diagonals from Householder QR factorization: The QR factorization routines now guarantee that the diagonal is both real and non-negative.
High Performance QR and Householder Reflections on Low-Profile Matrices : The auxiliary routines to apply Householder reflections (e.g. DLARFB) automatically reduce the cost of QR from O(n3) to O(n2+nb2) for matrices stored in a dense format for band matrices with bandwidth b with no user interface changes.
New fast and accurate Jacobi SVD : High accuracy SVD routine for dense matrices, which can compute tiny singular values to many more correct digits than xGESVD when the matrix has columns differing widely in norm, and usually runs faster than xGESVD too
Routines for Rectangular Full Packed format: The RFP format (SF, HF, PF, TF) enables efficient routines with optimal storage for symmetric, Hermitian or triangular matrices.
Pivoted Cholesky: Pivoting is required for reliable rank detection.
Mixed precision iterative refinement (Cholesky).
More robust DQDS algorithm: Routines for exploiting fast single precision hardware.