ANL-CEEESA/MIPLearn.jl
1
0
Fork 0
mirror of https://github.com/ANL-CEEESA/MIPLearn.jl.git synced 2025-12-06 16:38:51 -06:00
Code Issues Packages Projects Releases Wiki Activity

compute_tableau: Compute directly in compressed row format

Browse Source
This commit is contained in:
Alinson S. Xavier
2025-08-08 15:30:01 -05:00
parent 5e2b0c2958
commit bb59362571
1 changed files with 40 additions and 41 deletions
Download Patch File Download Diff File Expand all files Collapse all files

81
src/Cuts/tableau/tableau.jl
View File

@@ -74,15 +74,15 @@ function compute_tableau(
factor = klu(sparse(lhs_b')) factor = klu(sparse(lhs_b'))
end end
@timeit "Initialize sparse arrays" begin @timeit "Initialize arrays" begin
tableau_rhs::Array{Float64} = zeros(length(rows)) num_rows = length(rows)
tableau_lhs_I::Array{Int} = Int[] tableau_rhs::Array{Float64} = zeros(num_rows)
tableau_lhs_J::Array{Int} = Int[] tableau_rowptr::Array{Int} = zeros(Int, num_rows + 1)
tableau_lhs_V::Array{Float64} = Float64[] tableau_colval::Array{Int} = Int[]
estimated_nnz::Int = round(length(rows) * ncols * estimated_density) tableau_nzval::Array{Float64} = Float64[]
sizehint!(tableau_lhs_I, estimated_nnz) estimated_nnz::Int = round(num_rows * ncols * estimated_density)
sizehint!(tableau_lhs_J, estimated_nnz) sizehint!(tableau_colval, estimated_nnz)
sizehint!(tableau_lhs_V, estimated_nnz) sizehint!(tableau_nzval, estimated_nnz)
e::Array{Float64} = zeros(nrows) e::Array{Float64} = zeros(nrows)
sol::Array{Float64} = zeros(nrows) sol::Array{Float64} = zeros(nrows)
tableau_row::Array{Float64} = zeros(ncols) tableau_row::Array{Float64} = zeros(ncols)
@@ -90,49 +90,48 @@ function compute_tableau(
A = data.constr_lhs' A = data.constr_lhs'
b = data.constr_ub b = data.constr_ub
tableau_rowptr[1] = 1
for k in eachindex(rows) @timeit "Process rows" begin
@timeit "Solve" begin for k in eachindex(rows)
fill!(e, 0.0) @timeit "Solve" begin
e[rows[k]] = 1.0 fill!(e, 0.0)
ldiv!(sol, factor, e) e[rows[k]] = 1.0
end ldiv!(sol, factor, e)
@timeit "Compute row" begin
mul!(tableau_row, A, sol)
tableau_rhs[k] = dot(sol, b)
end
needed_space = length(tableau_lhs_I) + ncols
if needed_space > estimated_nnz
@timeit "Grow arrays" begin
estimated_nnz *= 2
sizehint!(tableau_lhs_I, estimated_nnz)
sizehint!(tableau_lhs_J, estimated_nnz)
sizehint!(tableau_lhs_V, estimated_nnz)
end end
end @timeit "Compute row" begin
mul!(tableau_row, A, sol)
@timeit "Collect nonzeros" begin tableau_rhs[k] = dot(sol, b)
for j in 1:ncols end
val = tableau_row[j] needed_space = length(tableau_colval) + ncols
if abs(val) > tol if needed_space > estimated_nnz
push!(tableau_lhs_I, k) @timeit "Grow arrays" begin
push!(tableau_lhs_J, j) estimated_nnz *= 2
push!(tableau_lhs_V, val) sizehint!(tableau_colval, estimated_nnz)
sizehint!(tableau_nzval, estimated_nnz)
end end
end end
@timeit "Collect nonzeros for row" begin
for j in 1:ncols
val = tableau_row[j]
if abs(val) > tol
push!(tableau_colval, j)
push!(tableau_nzval, val)
end
end
end
tableau_rowptr[k + 1] = length(tableau_colval) + 1
end end
end end
@timeit "Shrink arrays" begin @timeit "Shrink arrays" begin
sizehint!(tableau_lhs_I, 0) sizehint!(tableau_colval, length(tableau_colval))
sizehint!(tableau_lhs_J, 0) sizehint!(tableau_nzval, length(tableau_nzval))
sizehint!(tableau_lhs_V, 0)
end end
@timeit "Build sparse matrix" begin @timeit "Build sparse matrix" begin
tableau_lhs = sparse(tableau_lhs_I, tableau_lhs_J, tableau_lhs_V, length(rows), ncols) tableau_lhs_transposed = SparseMatrixCSC(ncols, num_rows, tableau_rowptr, tableau_colval, tableau_nzval)
tableau_lhs = transpose(tableau_lhs_transposed)
end end
@timeit "Compute tableau objective row" begin @timeit "Compute tableau objective row" begin