HSL

These are the beginnings of a set of interfaces to HSL packages for sparse linear algebra.

Certain HSL packages are freely available to all, others are freely available to academics only. Please refer to the website above for licensing information. In all cases, users are responsible for obtaining HSL packages.

Installing

julia> ]
pkg> add HSL

At this point, make sure that there isn't a stray METIS library on your library path. You especially want to make sure that METIS 5 is not accessible because the HSL library currently interfaced only supports METIS 4. If you have such library accessible, it is important to remove it from the library path, at least temporarily. For example, if you are on OSX and are using Homebrew, you can hide METIS 5 with brew unlink metis. After the install procedure is complete, it is fine to link metis again with brew link metis.

Set the environment variables HSL_MA57_PATH and HSL_MA97_PATH to specify where the source archives tar.gz are stored. The HSL Julia module will take care of compilation. Once the source archives have been placed in the locations indicated by the environment variables, run

julia> ]
pkg> build HSL
pkg> test HSL

Note that a C and Fortran compilers are required.

Supported Packages

HSL_MA97

HSL_MA97 version 2.6.0: an OpenMP-based direct solver for symmetric linear systems. Example:

using MatrixMarket
using HSL

K = MatrixMarket.mmread("K.mtx")  # only the lower triangle
rhs = readdlm("rhs.rhs")

LBL = Ma97(K)
ma97_factorize!(LBL)
x = ma97_solve(LBL, rhs)  # or x = LBL \ rhs

Rectangular Systems

There is a convenience interface to solve rectangular systems that complements the sparse QR factorization in Julia.

When A is m-by-n with m < n and has full row rank,

(x, y) = ma97_solve(A, b)

solves for the minimum-norm solution, i.e., x such that Ax = b and x + Aᵀ y = 0. The call

(x, y) = ma97_min_norm(A, b)

is also defined, and is equivalent to the above.

When m > n and has full column rank,

(r, x) = ma97_solve(A, b)

solves for the least-squares solution, i.e., x such that r = b - Ax satisfies Aᵀ r = 0. The call

(r, x) = ma97_least_squares(A, b)

is also defined, and is equivalent to the above.

HSL_MA57

HSL_MA57 version 5.2.0: a sparse, multifrontal solver for symmetric linear systems. Example:

using MatrixMarket
using HSL

K = MatrixMarket.mmread("examples/K_0.mtx")  # only the lower triangle
rhs = readdlm("examples/rhs_0.rhs")
rhss = hcat(rhs, rhs)

## factorize and solve
LDL = Ma57(K)
ma57_factorize(LDL)
LDL.info.rank
x = ma57_solve(LDL, rhs)  # or x = LBL \ rhs
norm(K*x - rhs)
xx = ma57_solve(LDL, rhss)  # or x = LBL \ rhss

Rectangular Systems

There is a convenience interface to solve rectangular systems that complements the sparse QR factorization in Julia.

When A is m-by-n with m < n and has full row rank,

(x, y) = ma57_solve(A, b)

solves for the minimum-norm solution, i.e., x such that Ax = b and x + Aᵀ y = 0. The call

(x, y) = ma57_min_norm(A, b)

is also defined, and is equivalent to the above.

When m > n and has full column rank,

(r, x) = ma57_solve(A, b)

solves for the least-squares solution, i.e., x such that r = b - Ax satisfies Aᵀ r = 0. The call

(r, x) = ma57_least_squares(A, b)

is also defined, and is equivalent to the above. Example:

using MatrixMarket
using HSL

K = MatrixMarket.mmread("examples/K_0.mtx")  # only the lower triangle
rhs = readdlm("examples/rhs_0.rhs")

## solve min norm
K_mn = K[1:200,:]
x_mn, y_mn = ma57_min_norm(K_mn, rhs[1:200]) # == ma57_solve(K_mn, rhs[1:200])

## solve least squares
K_ls = K[:,1:200]
r_ls, x_ls = ma57_least_squares(K_ls, rhs)   # == ma57_solve(K_ls, rhs)

TODO List

• Convenient constructor for rectangular matrices to enable \
• Convenient access to control parameters, especially pivot tolerance
• Real single precision arithmetic (e59c501)
• Complex single precision arithmetic (e59c501)
• Complex double precision arithmetic (e59c501)