# Linear Algebra in PSI4

## How to call BLAS & LAPACK in PSI4

Computational chemistry is essentially linear algebra on molecular systems, so using stable, portable, scalable, and efficient numerical linear algebra methods in PSI4 is critical. To that end, we use BLAS1 (vector-vector operations, like dot products), BLAS2 (matrix-vector operations, like rank-1 update), BLAS3 (matrix-matrix operations, like matrix multiplication), and LAPACK (advanced matrix decompositions and solutions). The methods provided by BLAS and LAPACK are standard, but the performance of actual implementations differ greatly from one version to another. Moreover, the standard interfaces to the libraries are Fortran, so PSI4 provides a common set of wrappers in psi4/psi4/src/psi4/libqt/qt.h .

Warning

Although block_matrix, init_array, and print_mat are still around, their use is discouraged in favor of operations on psi4.core.Matrix itself. The advice in these docs will catch up shortly.

### BLAS Wrappers

BLAS wrappers are currently fully supported at double precision.

BLAS commands involving matrices are wrapped so as to be conventional C-style “row-major” indexing, meaning that the column is the fast index like normal.

- The calls to BLAS1 routines are wrapped so as to allow for operations on vectors with more than 2^{31} elements (~16 GB, getting to be a problem). So passing a signed or unsigned long works, though the stride arguments must be integers.

- All routines are declared in `qt.h`. Each routine is prefixed with a `C_`, followed by the standard Fortran name of the routine, in capital letters. Input parameters of single primitives (`int`, `double`, `unsigned long int`, `char`, …) are passed by value. Arrays, including multidimensional arrays, are required to be in contiguous memory (as provided by block_matrix, for example), and are passed by providing a pointer to the first double or int element of the data (this is array[0] if array is `double**`). BLAS1 routines occasionally return values (DDOT for instance), BLAS2 and BLAS3 always return void. For char arguments, case is insensitive. A few examples are provided:

       1
       2
       3
       4
       5
       6
       7
       8
       9
      10
      11
      12
      13
      14
      15
      16
      17
      18
      19
      20
      21
      22
      23
      24
      25
      26
      27
      28
      29
      30
      31
      32
      33
      34
      35
      36
      37
      38
      39

      // BLAS/LAPACK
      #include "psi4/libqt/qt.h"
      // block_matrix, init_array
      #include "psi4/libciomr/libciomr.h"

      using namespace psi;
      ...

      // Allocate a,b vectors
      int n = 100;
      double* a = init_array(n);
      double* b = init_array(n);

      // Allocate A matrix;
      double** A = block_matrix(n,n);
      double** B = block_matrix(n,n);
      double** C = block_matrix(n,n);

      // Call the BLAS1 dot product between a and b
      // n can be a ULI with the BLAS1 wrappers,
      // All strides must be ints though
      double dot = C_DDOT(n, a, 1, b, 1);

      // Call the BLAS2 GEMV without transposition
      // Note this works in row-major order
      C_DGEMV('N', n, n, 1.0, A[0], n, a, 1, 0.0, b, 1);

      // Call the BLAS3 GEMM without transposition
      // Note this works in row-major order
      C_DGEMM('N','N', n, n, n, 1.0, A[0], n, B[0], n, 0.0, C[0], n);

      // Array's init'd with init_array must be free'd, not delete[]'d
      free(a);
      free(b);

      // Block matrix should be free_blocked
      free_block(A);
      free_block(B);
      free_block(C);

### Important BLAS Routines

- BLAS1
  - DDOT: dot product
  - DCOPY: efficient memory copy (with variable stride)
  - DAXPY: y = y + alpha*x
  - DROT: Givens Rotation
  - DNRM2: Vector norm square
- BLAS2
  - DGEMV: General Matrix-Vector product
  - DTRMV: Triangular Matrix-Vector product (2x faster, not wrapped yet)
  - DTRSM: Triangular Matrix-Vector solution via back substitution (just as fast as DTRMV)
  - DGER: Rank-1 update (not wrapped yet)
- BLAS3
  - DGEMM: General Matrix-Matrix product
  - DTRMM: General Triangular Matrix-General Matrix product
  - DTRSM: Triangular Matrix-General Matrix solution via back substitution (just as fast as DTRMM)
  - DSYMM/DSYMV calls are not appreciably faster than DGEMM calls, and should only be used in expert situations (like using the other half of the matrix for some form of other transformation).
  - DTRMM/DTRMV calls are 2x faster than DGEMM, and should be used where possible.

### LAPACK Wrappers

All standard LAPACK 3.2 double precision routines are provided.

LAPACK commands remain in Fortran’s “column-major” indexing, so all the results will be transposed, and leading dimensions may have to be fiddled with (using `lda = n` in both directions for square matrices is highly recommended). An example of the former problem is a Cholesky Decomposition: you expect to get back a lower triangular matrix L such that `L L^T = A`, but this is returned in column-major order, so the actual recovery of the matrix A with the row-major BLAS wrappers effectively involves `L^T L = A`. On of the biggest consequences is in linear equations: The input/output forcing/solution vector must be explicitly formed in column-major indexing (each vector is placed in a C++ row, with its entries along the C++ column). This is visualized in C++ as the transpose of the forcing/solution vector. All routines are declared in qt.h. Each routine is prefixed with a `C_`, followed by the standard Fortran name of the routine, in capital letters. Input parameters of single primitives (int, double, unsigned long int, char, …) are passed by value. Arrays, including multidimensional arrays, are required to be in contiguous memory (as provided by block_matrix, for example), and are passed by providing a pointer to the first double or int element of the data (this is array[0] if array is `double**`). All routines return an int INFO with error and calculation information specific to the routine, In Fortran, this is the last argument in all LAPACK calls, but should not be provided as an argument here. For char arguments, case is insensitive. A Cholesky transform example is shown:

     1
     2
     3
     4
     5
     6
     7
     8
     9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21
    22
    23
    24
    25
    26
    27
    28
    29
    30
    31
    32
    33

    // BLAS/LAPACK
    #include "psi4/libqt/qt.h"
    // block_matrix, init_array
    #include "psi4/libciomr/libciomr.h"

    using namespace psi;
    ...
    int n = 100;

    // Allocate A matrix;
    double** A = block_matrix(n,n);

    // Call the LAPACK DPOTRF to get the Cholesky factor
    // Note this works in column-major order
    // The result fills like:
    //   * * * *
    //     * * *
    //       * *
    //         *
    // instead of the expected:
    //   *
    //   * *
    //   * * *
    //   * * * *
    //
    int info = C_DPOTRF('L', n, A[0], n);

    // A bit painful, see below
    fprintf(outfile, "A:\n");
    print_mat(A,n,n,outfile);

    // Block matrix should be free_blocked
    free_block(A);

### Important Lapack Routines

- DSYEV: Eigenvalues and, optionally eigenvectors of a symmetric matrix. Eigenvectors take up to 10x longer than eigenvalues.
- DGEEV: Eigenvalues and, optionally eigenvectors of a general matrix. Up to 10x slower than DSYEV.
- DGESV: General solver (uses LU decomposition).
- DGESVD: General singular value decomposition.
- DGETRF: LU decomposition.
- DPOTRF: Cholesky decomposition (much more stable/faster)
- DGETRS: Solver, given LU decomposition by DGETRF
- DPOTRS: Solver, given Cholesky decomposition by DPOTRF
- DGETRI: Inverse, given LU decomposition by DGETRF (Warning: it’s faster and more stable just to solve with DGETRS)
- DPOTRI: Inverse, given Cholesky decomposition by DPOTRF (Warning: it’s faster and more stable just to solve with DPOTRS)

## How to use low-level BLAS/LAPACK with `psi4.core.Matrix`

Jet’s awesome new Matrix object has a lot of simple BLAS/LAPACK built in, but you can just as easily use the `double***` array underneath if you are careful (the outer index is the submatrix for each irrep). Here’s an example:

     1
     2
     3
     4
     5
     6
     7
     8
     9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21
    22
    23
    24
    25
    26
    27
    28
    29
    30
    31
    32
    33

    // BLAS/LAPACK
    #include "psi4/libqt/qt.h"
    // Matrix
    #include "psi4/libmints/matrix.h"

    using namespace psi;
    ...
    int n = 100;

    // Allocate A Matrix (new C1 convenience constructor);
    shared_ptr<Matrix> A(new Matrix("Still A, but way cooler", n,n));
    // Get the pointer to the 0 irrep (C1 for now, it errors if you ask for too high of an index)
    double** A_pointer = A->get_pointer(0);

    // Call the LAPACK DPOTRF to get the Cholesky factor
    // Note this works in column-major order
    // The result fills like:
    //   * * * *
    //     * * *
    //       * *
    //         *
    // instead of the expected:
    //   *
    //   * *
    //   * * *
    //   * * * *
    //
    int info = C_DPOTRF('L', n, A_pointer[0], n);

    // Wow that's a lot easier
    A->print();

    // Don't free, it's shared_ptr!

## How to name orbital bases (e.g., AO & SO)

Many different working bases (the internal linear algebraic basis, not the name of the Gaussian basis) are used within PSI4, each with a unique and important purpose. It is critical to keep them all distinct to prevent weird results from occurring.

- `AO` (Atomic Orbitals): Cartesian Gaussians (6D, 10F, etc.), `(L + 1)(L + 2)/2` functions per shell of angular momentum L. The ordering of Cartesian exponents for a given L is given by the standard ordering below (MATLAB code):

       1
       2
       3
       4
       5
       6
       7
       8
       9
      10
      11
      12

      ncart = (L + 1) * (L + 2) / 2;
      exps = zeros(ncart,3);
      index = 1;
      for i = 0:L
          for j = 0:i
              lx = L - i;
              ly = i - j;
              lz = j;
              exps(index,:) = [lx ly lz];
            index = index + 1;
          end
      end

- `SO` (Spherical Atomic Orbitals): Pure Gaussians (5D, 7F, etc.) or Cartesian Gaussians, as determined by the user. This is typically the first layer encountered, Libmints handles the transform from AO to SO automatically. If Cartesian functions are used, the number of functions per shell remains `(L + 1)(L + 2)/2`, and the ordering remains the same as above. Note that the individual functions are not normalized for angular momentum as in most codes: the self-overlap of a PSI4 Cartesian D or higher function with more than one nonzero Cartesian exponent (e.g., lx = 1, ly = 1, lz = 0) will be less than one. If Spherical Harmonics are used, 2L + 1 real combinations of the spherical harmonics are built from the `(L+1)(L+2)/2` Cartesian Gaussians, according to H. Schlegel and M. Frish, IJQC, 54, 83-87, 1995. Unlike Cartesian functions these functions are all strictly normalized. Note that in PSI4, the real combinations of spherical harmonic functions (see the paragraph below Eq. 15 in the Schlegel paper) are ordered as: 0, 1+, 1-, 2+, 2-, ….

- `USO` (Unique Symmetry-Adapted Orbitals): Spatial symmetry-adapted combinations of SOs, blocked according to irrep. The total number of USOs is the same as the number of SOs, but the number of USOs within each irrep is usually much smaller, which can lead to significant performance improvements. Note that this basis is sometimes unfortunately referred to as the SO basis, so it’s a bit context specific.

- `OSO` (Orthogonal Symmetry-Adapted Orbitals): USOs orthogonalized by Symmetric or Canonical Orthogonalization. The number of OSOs may be slightly smaller than the total number of USOs, due to removal of linear dependencies via Canonical Orthogonalization. The OSOs are rarely encountered, as usually we go straight from USOs to MOs.

- `MO` (Molecular Orbitals): The combination of OSOs that diagonalizes the Fock Matrix, so each basis function is a Hartree-Fock (or Kohn-Sham) molecular orbital. The number of OSOs and MOs is always the same. MOs are orthonormal.

- `LO` (Localized Orbitals): Localized occupied orbitals, a different combination of the occupied molecular orbitals which enhances spatial locality. LOs do not diagonalize the occ-occ block of the Fock Matrix, but remain orthonormal to each other and the virtual space.

## How to name orbital dimensions

There are a number of different names used to refer to the basis set size. These may seem redundant, but they have subtly different meanings, as detailed below.

A calculation can use either pure (5D, 7F, 9G, etc.) basis functions or Cartesian (6D, 10F, 15G, etc.), as dictated by the input file / basis set specification. Also, the basis can be represented in terms of atomic orbitals (AO) or symmetry-adapted orbitals (SO). Further complications come from the fact that a nearly linearly-dependent basis set will have functions removed from it to prevent redundancies. With all of these factors in mind, here are the conventions used internally:

- nao — The number of atomic orbitals in Cartesian representation.
- nso — The number of atomic orbitals but in the pure representation if the current basis uses pure functions, number of Cartesian AOs otherwise.
- nbf — The number of basis functions, which is the same as nso.
- nmo — The number of basis functions, after projecting out redundancies in the basis.

When molecular symmetry is utilized, a small array of sizes per irrep is usually allocated on the stack, and is named by augmenting the name above with a pi (per-irrep), e.g. nmopi. Note that the number of irreps is always the singular nirrep, and that the index variable h is always used in a for-loop traverse of irreps.

## How to name orbital spaces (e.g., docc)

As with basis sets, a number of names are used to refer to refer to the quantity of electrons, virtuals, and active sub-quantities of a PSI4 calculation. All of these can be defined per irrep as above. Some common conventions are:

- nelec — The number of electrons, rarely used due to specialization of alphas and betas or soccs and doccs.
- nalpha — The number of alpha electrons.
- nbeta — The number of beta electrons
- docc — The number of doubly-occupied orbitals
- socc — The number of singly-occupied orbitals (Almost always alpha, we don’t like open-shell singlets much).
- nvir — The number of virtual orbitals

### Multireference Dimensions

A orbital diagram of the nomenclature used for CI and MCSCF calculations.

Diagrammatically:

     1
     2
     3
     4
     5
     6
     7
     8
     9
    10
    11
    12
    13
    14
    15

    -----------------------------------------------
           CI       |      RAS      |     CAS
    -----------------------------------------------
                    | frozen_uocc   | frozen_uocc
    dropped_uocc    | rstr_uocc     | rstr_uocc
    -----------------------------------------------
                    | RAS IV        |
                    | RAS III       |
    active          |               | active
                    | RAS II        |
                    | RAS I         |
    -----------------------------------------------
    dropped_docc    | rstr_docc     | rstr_docc
                    | frozen_docc   | frozen_dcc
    -----------------------------------------------

Notation:

- uocc — Unoccupied orbitals.
- active — Variable occupation orbitals.
- socc — Singly occupied orbitals.
- docc — Doubly occupied orbitals.

Orbital spaces:

- frozen_uocc — Absolutely frozen virtual orbital.
- rstr_uocc — Can have rotations, no excitations into.
- dropped_uocc — rstr_uocc + frozen_uocc

—– end CI active —–

- RAS IV — uocc, limited number of excitations into.
- RAS III — uocc, limited number of excitations into.
- RAS II — docc/socc/uocc, equivalent to active in CAS.
- RAS I — docc/socc/uocc, limited excitations out of.

—– start CI active —–

- dropped_docc — rstr_docc + frozen_docc
- rstr_docc — Can have rotations, no excitations from.
- frozen_docc — Absolutely frozen core orbital.

Orbitals are sorted by space, irrep, eigenvalue.