Use qr_mumps_jll v3.1.1

Project.toml

@@ -27,7 +27,7 @@ SparseArrays = "1.10"
 SparseMatricesCOO = "0.2.2"
 Test = "1.10"
 julia = "1.10"
-qr_mumps_jll = "3.1.0"
+qr_mumps_jll = "3.1.1"
 
 [extras]
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"

README.md

@@ -1,4 +1,4 @@
-# A [Julia](http://julialang.org) Interface to [qr_mumps](http://qr_mumps.gitlab.io/)
+# A [Julia](http://julialang.org) Interface to [qr_mumps](https://qr_mumps.gitlab.io/)
 
 | **Documentation** | **Linux/macOS/Windows/FreeBSD** | **Coverage** | **DOI** |
 |:-----------------:|:-------------------------------:|:------------:|:-------:|
@@ -31,7 +31,7 @@ pkg> test QRMumps
 
 ## Content
 
-[qr_mumps](http://qr_mumps.gitlab.io/) is a software package for the solution of sparse, linear systems on multicore computers.
+[qr_mumps](https://qr_mumps.gitlab.io/) is a software package for the solution of sparse, linear systems on multicore computers.
 It implements a direct solution method based on the QR or Cholesky factorization of the input matrix. 
 Therefore, it is suited to solving sparse least-squares problems, to computing the minimum-norm solution of sparse, underdetermined problems and to solving symmetric, positive-definite sparse linear systems.
 It can obviously be used for solving square unsymmetric problems in which case the stability provided by the use of orthogonal transformations comes at the cost of a higher operation count with respect to solvers based on, e.g., the LU factorization such as [MUMPS](https://mumps-solver.org/index.php).

docs/src/index.md

@@ -1,6 +1,6 @@
 # Introduction
 
-This package provides a Julia interface to [qr_mumps](http://qr_mumps.gitlab.io/), a software for solving sparse linear systems on multicore computers.
+This package provides a Julia interface to [qr_mumps](https://qr_mumps.gitlab.io/), a software for solving sparse linear systems on multicore computers.
 qr\_mumps implements a direct solution method based on the QR or Cholesky factorization of the input matrix. 
 Therefore, it is suited to solving sparse least-squares problems, to computing the minimum-norm solution of sparse, underdetermined problems and to solving symmetric, positive-definite sparse linear systems. It can obviously be used for solving square unsymmetric problems in which case the stability provided by the use of orthogonal transformations comes at the cost of a higher operation count with respect to solvers based on, e.g., the LU factorization such as [MUMPS](https://mumps-solver.org/index.php). It supports real and complex, single or double precision arithmetic.
 

src/wrapper/qr_mumps_api.jl

@@ -5,8 +5,7 @@ for (fname, elty) in ((:sqrm_spfct_get_rp_c, :Float32   ),
     @eval begin
         function qrm_spfct_get_rp(spfct :: qrm_spfct{$elty})
             err = $fname(spfct, spfct.ptr_rp)
-            # Fix it with the release 3.0.5
-            # qrm_check(err)
+            qrm_check(err)
             rp = unsafe_wrap(Array, spfct.ptr_rp[], spfct.fct.m)
             return rp
         end
@@ -20,8 +19,7 @@ for (fname, elty) in ((:sqrm_spfct_get_cp_c, :Float32   ),
     @eval begin
         function qrm_spfct_get_cp(spfct :: qrm_spfct{$elty})
             err = $fname(spfct, spfct.ptr_cp)
-            # Fix it with the release 3.0.5
-            # qrm_check(err)
+            qrm_check(err)
             cp = unsafe_wrap(Array, spfct.ptr_cp[], spfct.fct.n)
             return cp
         end
@@ -113,7 +111,7 @@ for (fname, elty) in ((:sqrm_spmat_destroy_c, :Float32   ),
     @eval begin
         function qrm_spmat_destroy!(spmat :: qrm_spmat{$elty})
             err = $fname(spmat)
-            # qrm_check(err)
+            qrm_check(err)
             return nothing
         end
     end
@@ -886,7 +884,7 @@ function qrm_finalize()
 end
 
 function qrm_update!(spmat :: qrm_spmat{T}, val :: AbstractVector{T}) where T
-    spmat.val .= val
+    copyto!(spmat.val, val)
     return nothing
 end
 

test/test_qrm.jl

@@ -564,8 +564,8 @@ end
       qrm_set(spfct, "qrm_rd_eps", tol)
       qrm_analyse!(spmat, spfct)
       qrm_factorize!(spmat, spfct)
-      @test (@allocated qrm_get(spfct, "qrm_rd_num")) == 0
-
+      nbits = @allocated qrm_get(spfct, "qrm_rd_num")
+      @test nbits == 0
     end
   end