HiGHS replaced Clp

Author: BoundaryValueProblems

Project.toml

@@ -6,11 +6,11 @@ version = "1.7.4"
 [deps]
 Arpack = "7d9fca2a-8960-54d3-9f78-7d1dccf2cb97"
 AverageShiftedHistograms = "77b51b56-6f8f-5c3a-9cb4-d71f9594ea6e"
-Clp = "e2554f3b-3117-50c0-817c-e040a3ddf72d"
 Clustering = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5"
 Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
+HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"
 JLD = "4138dd39-2aa7-5051-a626-17a0bb65d9c8"
 JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
@@ -27,11 +27,11 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 [compat]
 Arpack = "0.4.0, 0.5.3"
 AverageShiftedHistograms = "0.8.7"
-Clp = "1.0.1"
 Clustering = "0.14.2, 0.15"
 Distances = "0.10.2"
 Distributions = "0.24.18, 0.25"
 Graphs = "1.6.0"
+HiGHS = ">= 1"
 JLD = "0.12.3, 0.13"
 JLD2 = "0.4.9, 0.6"
 JuMP = "0.21.3, 0.22, 0.23, 1"

src/MultiscaleGraphSignalTransforms.jl

@@ -23,7 +23,7 @@ export cost_functional, dmatrix_flatten, dmatrix_ldb_flatten
 
 ## export functions of NGWP.jl
 using LinearAlgebra, SparseArrays, Graphs, SimpleWeightedGraphs, Clustering
-using JuMP, Clp, Optim, Statistics, QuadGK, Arpack
+using JuMP, HiGHS, Optim, Statistics, QuadGK, Arpack
 import Plots: plot, plot!, scatter, scatter!
 import StatsBase: crosscor
 

src/eigROT_Distance.jl

@@ -27,7 +27,7 @@ function eigROT_Distance(P::Matrix{Float64}, Q::SparseMatrixCSC{Int64,Int64};
     m2 = size(Q2, 2)
     for i = 1:(n - 1), j = (i + 1):n
         f = P[:, i] - P[:, j]
-        md = Model(optimizer_with_attributes(Clp.Optimizer, "LogLevel" => 0))
+        md = Model(optimizer_with_attributes(HiGHS.Optimizer, "LogLevel" => 0))
         @variable(md, w[1:m2] >= 0.0)
         edge_length == 1 ? @objective(md, Min, sum(w)) : @objective(md, Min, sum(w .* le2))
         @constraint(md, Q2 * w .== f)
@@ -68,7 +68,7 @@ function ROT_Distance(A::Any, B::Any, Q::SparseMatrixCSC{Int64,Int64};
     m2 = size(Q2, 2)
     for i = 1:m, j = 1:n
         f = (ndims(A) > 1 && ndims(B) > 1) ? B[:,j] - A[:,i] : B - A
-        md = Model(optimizer_with_attributes(Clp.Optimizer, "LogLevel" => 0))
+        md = Model(optimizer_with_attributes(HiGHS.Optimizer, "LogLevel" => 0))
         @variable(md, w[1:m2] >= 0.0)
         edge_length == 1 ? @objective(md, Min, sum(w)) : @objective(md, Min, sum(w .* le2))
         @constraint(md, Q2 * w .== f)