removed null space related code as obsolete

src/solvers.jl

@@ -184,55 +184,6 @@ function get_boundary_assembly(solver::Solver)
     return K, C1, C2, D, f, g
 end
 
-function resize!(A::SparseMatrixCSC, m::Int64, n::Int64)
-    (n == A.n) && (m == A.m) && return
-    @assert n >= A.n
-    @assert m >= A.m
-    append!(A.colptr, A.colptr[end]*ones(Int, m-A.m))
-    A.n = n
-    A.m = m
-end
-
-"""
-Given C and g, construct new basis such that v = P*u + g
-
-Parameters
-----------
-S  set of linearly independent dofs.
-"""
-function create_projection(C::SparseMatrixCSC, g; S=nothing, tol=1.0e-12)
-    n, m = size(C)
-    @assert n == m
-    if S == nothing
-        S = get_nonzero_rows(C)
-    end
-    # FIXME: this creates dense matrices
-    # efficiency / memory usage is a question
-    M = get_nonzero_columns(C)
-    F = qrfact(C[S,:])
-    P = spzeros(n,m)
-    P[:,M] = sparse(F \ full(C[S,M]))
-    h = sparse(F \ full(g[S]))
-    resize!(P, n, m)
-    resize!(h, n, 1)
-    P = speye(n) - P
-    droptol!(P, tol)
-    return P, h
-end
-
-""" Assume C is invertible. """
-function create_projection(C, g, ::Type{Val{:invertible}})
-    nz1, nz2 = get_nonzeros(C)
-    P = spzeros(size(C)...)
-    for j=1:size(C,1)
-        j in nz1 && continue
-        P[j,j] = 1.0
-    end
-    v = lufact(C[nz1,nz2]) \ full(g[nz1])
-    return P, v
-end
-
-
 
 """
 Solve linear system using LDLt factorization (SuiteSparse). This version

src/sparse.jl

@@ -178,11 +178,21 @@ function size(A::SparseMatrixCOO, idx::Int)
     return size(A)[idx]
 end
 
+""" Resize sparse matrix A to (higher) dimension n x m. """
+function resize_sparse(A, n, m)
+    return sparse(findnz(A)..., n, m)
+end
+
+""" Resize sparse vector b to (higher) dimension n. """
+function resize_sparsevec(b, n)
+    return sparsevec(findnz(b)..., n)
+end
+
 """ Matrix norm. Automatically convert to dense when asking for 2-norm for small matrices. """
 function norm(A::SparseMatrixCOO, p=Inf; maxdim=1000)
     dim = size(A, 1)
     if p == 2 && dim > maxdim
-        info("Assembly norm: dim = $dim > $maxdim and p=$p, not making dense matrices for operation.")
+        warn("Assembly norm: dim = $dim > $maxdim and p=$p, not making dense matrices for operation.")
         return 0.0
     end
     if p == 2
@@ -192,9 +202,9 @@ function norm(A::SparseMatrixCOO, p=Inf; maxdim=1000)
     end
 end
 
+""" Approximative comparison of two matricse A and B. """
 function isapprox(A::SparseMatrixCOO, B::SparseMatrixCOO)
     A2 = sparse(A)
     B2 = sparse(B, size(A2)...)
     return isapprox(A2, B2)
 end
-