Basic tidying of code

Project.toml

@@ -7,14 +7,12 @@ version = "0.16.2"
 [deps]
 LazyArtifacts = "4af54fe1-eca0-43a8-85a7-787d91b784e3"
 Libdl = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
-LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 
 [compat]
 LazyArtifacts = "<0.0.1, 1.6"
 Libdl = "<0.0.1, 1.6"
-LinearAlgebra = "<0.0.1, 1.6"
 MathOptInterface = "1"
 Random = "<0.0.1, 1.6"
 SparseArrays = "<0.0.1, 1.6"

deps/build.jl

@@ -11,19 +11,6 @@ if isfile(depsfile)
     rm(depsfile)
 end
 
-
-function my_download(url::AbstractString, filename::AbstractString)
-    if Sys.iswindows() && VERSION < v"1.1"
-        # from https://github.com/JuliaLang/julia/blob/788b2c77c10c2160f4794a4d4b6b81a95a90940c/base/download.jl#L26
-        # otherwise files are not properly downloaded - check sizes
-        curl_exe = joinpath(get(ENV, "SYSTEMROOT", "C:\\Windows"), "System32\\curl.exe")
-        process = run(`$curl_exe -s -S -g -L -f -o $filename $url`)#, wait=false)
-        return filename
-    else
-        return download(url, filename)
-    end
-end
-
 function write_depsfile(path)
     f = open(depsfile,"w")
     if Sys.iswindows()

src/MOI/MOI_wrapper.jl

@@ -3,15 +3,6 @@
 # Use of this source code is governed by an MIT-style license that can be found
 # in the LICENSE.md file or at https://opensource.org/licenses/MIT.
 
-@static if v"1.2" > VERSION >= v"1.1"
-    # see: https://github.com/jump-dev/Xpress.jl/pull/44#issuecomment-585882858
-    error("Versions 1.1.x of julia are not supported. The current verions is $(VERSION)")
-end
-
-import MathOptInterface
-using SparseArrays
-
-const MOI = MathOptInterface
 const CleverDicts = MOI.Utilities.CleverDicts
 
 @enum(
@@ -161,7 +152,7 @@
     var_status::Vector{Cint}
 end
 
-mutable struct SensitivityCache 
+mutable struct SensitivityCache
     input::Vector{Float64}
     output::Vector{Float64}
     is_updated::Bool
@@ -240,7 +231,7 @@
 
     # TODO: add functionality to the lower-level API to support querying single
     # elements of the solution.
-    
+
     cached_solution::Union{Nothing, CachedSolution}
     basis_status::Union{Nothing, BasisStatus}
     conflict::Union{Nothing, IISData}
@@ -391,7 +382,7 @@
     model.termination_status != MOI.OPTIMIZE_NOT_CALLED && return false
     model.primal_status != MOI.NO_SOLUTION && return false
     model.dual_status != MOI.NO_SOLUTION && return false
-    
+
     model.callback_cached_solution !== nothing && return false
     # model.cb_cut_data !== nothing && return false
     model.callback_state != CB_NONE && return false
@@ -816,7 +807,7 @@
         0,#length(_dmatval)::Int,
         Ref(0.0),#_dobj::Vector{Float64},
         C_NULL,#Cint.(_mrwind::Vector{Int}),
-        C_NULL,#Cint.(_mrstart::Vector{Int}), 
+        C_NULL,#Cint.(_mrstart::Vector{Int}),
         C_NULL,#_dmatval::Vector{Float64},
         Ref(-Inf),#_dbdl::Vector{Float64},
         Ref(Inf),#_dbdu::Vector{Float64}
@@ -831,7 +822,7 @@
         0,#length(_dmatval)::Int,
         zeros(N),# _dobj::Vector{Float64},
         C_NULL,#Cint.(_mrwind::Vector{Int}),
-        C_NULL,#Cint.(_mrstart::Vector{Int}), 
+        C_NULL,#Cint.(_mrstart::Vector{Int}),
         C_NULL,# _dmatval::Vector{Float64},
         fill(-Inf, N),# _dbdl::Vector{Float64},
         fill(Inf, N),# _dbdu::Vector{Float64}
@@ -1005,7 +996,7 @@
     rows = @_invoke Lib.XPRSgetintattrib(model.inner, Lib.XPRS_ROWS, _)::Int
     cols = @_invoke Lib.XPRSgetintattrib(model.inner, Lib.XPRS_COLS, _)::Int
     if model.forward_sensitivity_cache === nothing
-        model.forward_sensitivity_cache = 
+        model.forward_sensitivity_cache =
             SensitivityCache(
                 zeros(rows),
                 zeros(cols),
@@ -1023,10 +1014,10 @@
     if is_mip(model) && model.moi_warnings
         @warn "The problem is a MIP, it might fail to get correct sensitivities."
     end
-    if MOI.get(model, MOI.TerminationStatus()) != MOI.OPTIMAL 
+    if MOI.get(model, MOI.TerminationStatus()) != MOI.OPTIMAL
         error("Model not optimized. Cannot get sensitivities.")
     end
-    if model.forward_sensitivity_cache === nothing 
+    if model.forward_sensitivity_cache === nothing
         error("Forward sensitivity cache not initiliazed correctly.")
     end
     if model.forward_sensitivity_cache.is_updated != true
@@ -1042,7 +1033,7 @@
     rows = @_invoke Lib.XPRSgetintattrib(model.inner, Lib.XPRS_ROWS, _)::Int
     cols = @_invoke Lib.XPRSgetintattrib(model.inner, Lib.XPRS_COLS, _)::Int
     if model.backward_sensitivity_cache === nothing
-        model.backward_sensitivity_cache = 
+        model.backward_sensitivity_cache =
             SensitivityCache(
                 zeros(cols),
                 zeros(rows),
@@ -1060,10 +1051,10 @@
     if is_mip(model) && model.moi_warnings
         @warn "The problem is a MIP, it might fail to get correct sensitivities."
     end
-    if MOI.get(model, MOI.TerminationStatus()) != MOI.OPTIMAL 
+    if MOI.get(model, MOI.TerminationStatus()) != MOI.OPTIMAL
         error("Model not optimized. Cannot get sensitivities.")
     end
-    if model.backward_sensitivity_cache === nothing 
+    if model.backward_sensitivity_cache === nothing
         error("Backward sensitivity cache not initiliazed correctly.")
     end
     if model.backward_sensitivity_cache.is_updated != true
@@ -1623,7 +1614,7 @@
 function _set_variable_semi_lower_bound(model, info, value)
     info.semi_lower_bound = value
     _set_variable_lower_bound(model, info, 0.0)
-    @checked Lib.XPRSchgglblimit(model.inner, Cint(1), Ref(Cint(info.column-1)), 
+    @checked Lib.XPRSchgglblimit(model.inner, Cint(1), Ref(Cint(info.column-1)),
         Ref(Cdouble(value)))
 end
 
@@ -2045,8 +2036,8 @@
         Ref{UInt8}(sense),#_srowtype,
         Ref(rhs),#_drhs,
         C_NULL,#_drng,
-        Ref{Cint}(0),#Cint.(_mrstart::Vector{Int}), 
-        indices.-= 1,#Cint.(_mclind::Vector{Int}), 
+        Ref{Cint}(0),#Cint.(_mrstart::Vector{Int}),
+        indices.-= 1,#Cint.(_mclind::Vector{Int}),
         coefficients#_dmatval
     )
     return MOI.ConstraintIndex{typeof(f), typeof(s)}(model.last_constraint_index)
@@ -2090,16 +2081,16 @@
         model.affine_constraint_info[model.last_constraint_index] =
             ConstraintInfo(length(model.affine_constraint_info) + 1, si, AFFINE)
     end
-    pop!(row_starts)     
+    pop!(row_starts)
     @checked Lib.XPRSaddrows(
         model.inner,
         length(rhss),#length(_drhs),
         Cint(length(columns)),#Cint(length(_mclind)),
         senses,#_srowtype,
         rhss,#_drhs,
         C_NULL,#_drng,
-        row_starts.-= 1,#Cint.(_mrstart::Vector{Int}), 
-        columns.-= 1,#Cint.(_mclind::Vector{Int}), 
+        row_starts.-= 1,#Cint.(_mrstart::Vector{Int}),
+        columns.-= 1,#Cint.(_mclind::Vector{Int}),
         coefficients#_dmatval
         )
     return indices
@@ -2147,7 +2138,7 @@
     @checked Lib.XPRSchgrhs(
         model.inner,
         Cint(1),
-        Ref(Cint(_info(model, c).row - 1)), 
+        Ref(Cint(_info(model, c).row - 1)),
         Ref(MOI.constant(s)))
     return
 end
@@ -2157,7 +2148,7 @@
     nzcnt_max = Xpress.n_non_zero_elements(model.inner)
 
     _nzcnt = Ref(Cint(0))
-    @checked Lib.XPRSgetrows(model.inner, C_NULL, C_NULL, C_NULL, 0, _nzcnt, 
+    @checked Lib.XPRSgetrows(model.inner, C_NULL, C_NULL, C_NULL, 0, _nzcnt,
         Cint(row-1), Cint(row-1))
     nzcnt = _nzcnt[]
 
@@ -2392,8 +2383,8 @@
         Ref{UInt8}(sense),#_srowtype,
         Ref(rhs-cte),#_drhs,
         C_NULL,#_drng,
-        Ref{Cint}(0),#Cint.(_mrstart::Vector{Int}), 
-        indices.-= 1,#Cint.(_mrwind::Vector{Int}), 
+        Ref{Cint}(0),#Cint.(_mrstart::Vector{Int}),
+        indices.-= 1,#Cint.(_mrwind::Vector{Int}),
         coefficients#_dmatval
     )
     @checked Lib.XPRSsetindicators(model.inner,
@@ -2494,7 +2485,7 @@
     @checked Lib.XPRSgetqrowqmatrixtriplets(model.inner, _info(model, c).row-1, nqelem, mqcol1, mqcol2, dqe)
     mqcol1 .+= 1
     mqcol2 .+= 1
-    
+
     quadratic_terms = MOI.ScalarQuadraticTerm{Float64}[]
     for (i, j, coef) in zip(mqcol1, mqcol2, dqe)
         push!(
@@ -2617,7 +2608,7 @@
             V[j] = setvals[j]
         end
     end
-    return sparse(I, J, V, nsets[], n)
+    return SparseArrays.sparse(I, J, V, nsets[], n)
 end
 
 function MOI.get(
@@ -2799,7 +2790,7 @@
         has_Ray = Int64[0]
         @checked Lib.XPRSgetprimalray(model.inner, model.cached_solution.variable_primal, has_Ray)
         model.cached_solution.has_primal_certificate = _has_primal_ray(model)
-    elseif status == MOI.FEASIBLE_POINT 
+    elseif status == MOI.FEASIBLE_POINT
         model.cached_solution.has_feasible_point = true
     end
     status = MOI.get(model, MOI.DualStatus())
@@ -3021,7 +3012,7 @@
         end
     end
     return MOI.NO_SOLUTION
-    
+
 end
 
 function MOI.get(model::Optimizer, attr::MOI.DualStatus)
@@ -3554,7 +3545,7 @@
     for term in replacement.terms
         col = _info(model, term.variable).column
         @checked Lib.XPRSchgcoef(
-            model.inner, Cint(row-1), Cint(col-1), 
+            model.inner, Cint(row-1), Cint(col-1),
             MOI.coefficient(term)
         )
     end
@@ -3587,14 +3578,14 @@
     # First, zero out the old constraint function terms.
     for term in previous.terms
         col = _info(model, term.variable).column
-        @checked Lib.XPRSchgcoef(model.inner, Cint(row - 1), Cint(col - 1), 
+        @checked Lib.XPRSchgcoef(model.inner, Cint(row - 1), Cint(col - 1),
             0.0)
     end
 
     # Next, set the new constraint function terms.
     for term in previous.terms
         col = _info(model, term.variable).column
-        @checked Lib.XPRSchgcoef(model.inner, Cint(row - 1), Cint(col - 1), 
+        @checked Lib.XPRSchgcoef(model.inner, Cint(row - 1), Cint(col - 1),
             MOI.coefficient(term))
     end
     return
@@ -4432,4 +4423,4 @@
     MOI.check_result_index_bounds(model, attr)
     column = _info(model, vi).column
     return model.cached_solution.variable_dual[column]
-end
+end

src/Xpress.jl

@@ -7,6 +7,8 @@ module Xpress
 
 import LazyArtifacts
 import Libdl
+import MathOptInterface as MOI
+import SparseArrays
 
 # Load in `deps.jl`, complaining if it does not exist
 const depsjl_path = joinpath(@__DIR__, "..", "deps", "deps.jl")
@@ -43,8 +45,6 @@ else
     error("XPRESS cannot be loaded. Please run Pkg.build(\"Xpress\").")
 end
 
-import Base.show, Base.copy
-
 module Lib
     import ..Xpress
     const libxprs = Xpress.libxprs

test/Derivative/DerivativeExample.jl

@@ -3,11 +3,10 @@
 # Use of this source code is governed by an MIT-style license that can be found
 # in the LICENSE.md file or at https://opensource.org/licenses/MIT.
 
-using MathOptInterface
 using Xpress
 using Test
 
-const MOI = MathOptInterface
+import MathOptInterface as MOI
 
 #Example of simple Thermal Generation Dispatch and the derivatives of the generation by the demand (dg/dd)
 mutable struct DispachModel
@@ -27,7 +26,7 @@ function GenerateModel_VariableIndex()
     g_sup = [10.0, 20.0, 30.0] # Upper limit of generation for each generator
     c_g = [1.0, 3.0, 5.0]      # Cost of generation for each generator
     c_Df = 10.0                # Cost of deficit
-    
+
     # Creation of the Optimizer
     optimizer = Xpress.Optimizer(PRESOLVE=0, logfile = "outputXpress_SV.log")
     # Variables
@@ -43,7 +42,7 @@ function GenerateModel_VariableIndex()
     end
     c_limit_inf[I+1] = MOI.add_constraint(optimizer, Df, MOI.GreaterThan(0.0))
 
-    c_demand = MOI.add_constraint(optimizer, 
+    c_demand = MOI.add_constraint(optimizer,
         MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(ones(I+1),[g;Df]), 0.0),
         MOI.EqualTo(d)
     ) # Constraint of the Demand
@@ -68,7 +67,7 @@ function GenerateModel_ScalarAffineFunction()
     g_sup = [10.0, 20.0, 30.0] # Upper limit of generation for each generator
     c_g = [1.0, 3.0, 5.0]      # Cost of generation for each generator
     c_Df = 10.0                # Cost of deficit
-    
+
     # Creation of the Optimizer
     optimizer = Xpress.Optimizer(PRESOLVE=0, logfile = "outputXpress_SAF.log")
     # Variables
@@ -95,7 +94,7 @@ function GenerateModel_ScalarAffineFunction()
     c_limit_inf[I+1] = MOI.add_constraint(optimizer, MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(vectAux,[g;Df]),0.0), MOI.GreaterThan(0.0))
     vectAux[I+1] = 0.0
 
-    c_demand = MOI.add_constraint(optimizer, 
+    c_demand = MOI.add_constraint(optimizer,
         MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(ones(I+1),[g;Df]), 0.0),
         MOI.EqualTo(d)
     ) # Constraint of the Demand
@@ -121,10 +120,10 @@ function Forward(model::DispachModel, ϵ::Float64 = 1.0)
 
     #Get the primal solution of the model
     vect =  MOI.get.(optimizer, MOI.VariablePrimal(), vectRef)
-     
+
     #Pass the perturbation to the Xpress Framework and set the context to Forward
     MOI.set(optimizer, Xpress.ForwardSensitivityInputConstraint(), model.c_demand, ϵ)
-    
+
     #Get the derivative of the model
     dvect = MOI.get.(optimizer, Xpress.ForwardSensitivityOutputVariable(), vectRef)
 

test/MathOptInterface/MOI_callbacks.jl

@@ -4,11 +4,10 @@
 # in the LICENSE.md file or at https://opensource.org/licenses/MIT.
 
 using Xpress
-using MathOptInterface
-using Random
 using Test
 
-const MOI = MathOptInterface
+import MathOptInterface as MOI
+import Random
 
 function callback_simple_model()
     model = Xpress.Optimizer(
@@ -323,7 +322,7 @@ end
         cb_calls = Int32[]
         MOI.set(model, Xpress.CallbackFunction(), (cb_data) -> begin
             push!(cb_calls)
-            
+
             if Xpress.get_control_or_attribute(cb_data.model, Xpress.Lib.XPRS_CALLBACKCOUNT_OPTNODE) > 1
                 return
             end