Switch to MathOptInterface

.gitignore

@@ -6,3 +6,4 @@
 .ipynb_checkpoints/
 Manifest.toml
 benchmark/*.json
+test/Project.toml

Project.toml

@@ -6,26 +6,26 @@ version = "0.12.6"
 AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-MathProgBase = "fdba3010-5040-5b88-9595-932c9decdf73"
+MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
 OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]
 AbstractTrees = "^0.2.1"
 BenchmarkTools = "^0.4"
-MathProgBase = "^0.7"
+MathOptInterface = "~0.9"
 OrderedCollections = "^1.0"
 julia = "^1.0"
 
 [extras]
 ECOS = "e2685f51-7e38-5353-a97d-a921fd2c8199"
-GLPKMathProgInterface = "3c7084bd-78ad-589a-b5bb-dbd673274bea"
+GLPK = "60bf3e95-4087-53dc-ae20-288a0d20c6a6"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SCS = "c946c3f1-0d1f-5ce8-9dea-7daa1f7e2d13"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["ECOS", "GLPKMathProgInterface", "LinearAlgebra", "Random", "SCS", "Statistics", "Test"]
+test = ["ECOS", "GLPK", "LinearAlgebra", "Random", "SCS", "Statistics", "Test"]

README.md

@@ -5,7 +5,7 @@
 [![](https://img.shields.io/badge/docs-stable-blue.svg)](https://www.juliaopt.org/Convex.jl/stable)
 [![](https://img.shields.io/badge/docs-dev-blue.svg)](https://www.juliaopt.org/Convex.jl/dev)
 
-**Convex.jl** is a [Julia](http://julialang.org) package for [Disciplined Convex Programming](http://dcp.stanford.edu/). Convex.jl can solve linear programs, mixed-integer linear programs, and DCP-compliant convex programs using a variety of solvers, including [Mosek](https://github.com/JuliaOpt/Mosek.jl), [Gurobi](https://github.com/JuliaOpt/Gurobi.jl), [ECOS](https://github.com/JuliaOpt/ECOS.jl), [SCS](https://github.com/JuliaOpt/SCS.jl), and  [GLPK](https://github.com/JuliaOpt/GLPK.jl), through the [MathProgBase](http://mathprogbasejl.readthedocs.org/en/latest/) interface. It also supports optimization with complex variables and coefficients.
+**Convex.jl** is a [Julia](http://julialang.org) package for [Disciplined Convex Programming](http://dcp.stanford.edu/). Convex.jl can solve linear programs, mixed-integer linear programs, and DCP-compliant convex programs using a variety of solvers, including [Mosek](https://github.com/JuliaOpt/Mosek.jl), [Gurobi](https://github.com/JuliaOpt/Gurobi.jl), [ECOS](https://github.com/JuliaOpt/ECOS.jl), [SCS](https://github.com/JuliaOpt/SCS.jl), and  [GLPK](https://github.com/JuliaOpt/GLPK.jl), through [MathOptInterface](https://github.com/JuliaOpt/MathOptInterface.jl). It also supports optimization with complex variables and coefficients.
 
 **Installation**: `julia> Pkg.add("Convex")`
 
@@ -38,7 +38,7 @@ x = Variable(n)
 problem = minimize(sumsquares(A * x - b), [x >= 0])
 
 # Solve the problem by calling solve!
-solve!(problem, SCSSolver())
+solve!(problem, SCS.Optimizer())
 
 # Check the status of the problem
 problem.status # :Optimal, :Infeasible, :Unbounded etc.

benchmark/benchmarks.jl

@@ -2,12 +2,14 @@ using Pkg
 tempdir = mktempdir()
 Pkg.activate(tempdir)
 Pkg.develop(PackageSpec(path=joinpath(@__DIR__, "..")))
-Pkg.add(["BenchmarkTools", "PkgBenchmark"])
+Pkg.add(["BenchmarkTools", "PkgBenchmark", "MathOptInterface"])
 Pkg.resolve()
 
 using Convex: Convex, ProblemDepot
 using BenchmarkTools
-
+using MathOptInterface
+const MOI = MathOptInterface
+const MOIU = MOI.Utilities
 
 const SUITE = BenchmarkGroup()
 
@@ -30,4 +32,7 @@ problems =  [
                 "mip_integer_variables",
             ]
 
-SUITE["formulation"] = ProblemDepot.benchmark_suite(Convex.conic_problem, problems)
+SUITE["formulation"] = ProblemDepot.benchmark_suite(problems) do problem
+    model = MOIU.MockOptimizer(MOIU.Model{Float64}())
+    Convex.load_MOI_model!(model, problem)
+end

docs/Project.toml

@@ -6,7 +6,7 @@ DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 ECOS = "e2685f51-7e38-5353-a97d-a921fd2c8199"
-GLPKMathProgInterface = "3c7084bd-78ad-589a-b5bb-dbd673274bea"
+GLPK = "60bf3e95-4087-53dc-ae20-288a0d20c6a6"
 Interact = "c601a237-2ae4-5e1e-952c-7a85b0c7eef1"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"

docs/examples_literate/general_examples/basic_usage.jl

@@ -8,7 +8,7 @@ end
 
 using SCS
 ## passing in verbose=0 to hide output from SCS
-solver = SCSSolver(verbose=0)
+solver = SCS.Optimizer(verbose=0)
 
 # ### Linear program
 #
@@ -73,7 +73,7 @@ p.optval
 
 x = Variable(4)
 p = satisfy(norm(x) <= 100, exp(x[1]) <= 5, x[2] >= 7, geomean(x[3], x[4]) >= x[2])
-solve!(p, SCSSolver(verbose=0))
+solve!(p, SCS.Optimizer(verbose=0))
 println(p.status)
 x.value
 
@@ -82,7 +82,7 @@ x.value
 
 y = Semidefinite(2)
 p = maximize(lambdamin(y), tr(y)<=6)
-solve!(p, SCSSolver(verbose=0))
+solve!(p, SCS.Optimizer(verbose=0))
 p.optval
 
 #-
@@ -105,10 +105,10 @@ y.value
 # $$
 #
 
-using GLPKMathProgInterface
+using GLPK
 x = Variable(4, :Int)
 p = minimize(sum(x), x >= 0.5)
-solve!(p, GLPKSolverMIP())
+solve!(p, GLPK.Optimizer())
 x.value
 
 #-

docs/examples_literate/general_examples/chebyshev_center.jl

@@ -25,7 +25,7 @@ p.constraints += a1' * x_c + r * norm(a1, 2) <= b[1];
 p.constraints += a2' * x_c + r * norm(a2, 2) <= b[2];
 p.constraints += a3' * x_c + r * norm(a3, 2) <= b[3];
 p.constraints += a4' * x_c + r * norm(a4, 2) <= b[4];
-solve!(p, SCSSolver(verbose=0))
+solve!(p, SCS.Optimizer(verbose=0))
 p.optval
 
 # Generate the figure

docs/examples_literate/general_examples/control.jl

@@ -121,7 +121,7 @@ push!(constraints, velocity[:, T] == 0)
 
 ## Solve the problem
 problem = minimize(sumsquares(force), constraints)
-solve!(problem, SCSSolver(verbose=0))
+solve!(problem, SCS.Optimizer(verbose=0))
 
 # We can plot the trajectory taken by the object.
 

docs/examples_literate/general_examples/huber_regression.jl

@@ -40,18 +40,18 @@ for i=1:length(p_vals)
     fit = norm(beta - beta_true) / norm(beta_true);
     cost = norm(X' * beta - Y);
     prob = minimize(cost);
-    solve!(prob, SCSSolver(verbose=0));
+    solve!(prob, SCS.Optimizer(verbose=0));
     lsq_data[i] = evaluate(fit);
 
     ## Form and solve a prescient regression problem,
     ## i.e., where the sign changes are known.
     cost = norm(factor .* (X'*beta) - Y);
-    solve!(minimize(cost), SCSSolver(verbose=0))
+    solve!(minimize(cost), SCS.Optimizer(verbose=0))
     prescient_data[i] = evaluate(fit);
 
     ## Form and solve the Huber regression problem.
     cost = sum(huber(X' * beta - Y, 1));
-    solve!(minimize(cost), SCSSolver(verbose=0))
+    solve!(minimize(cost), SCS.Optimizer(verbose=0))
     huber_data[i] = evaluate(fit);
 end
 

docs/examples_literate/general_examples/logistic_regression.jl

@@ -23,7 +23,7 @@ X = hcat(ones(size(iris, 1)), iris.SepalLength, iris.SepalWidth, iris.PetalLengt
 n, p = size(X)
 beta = Variable(p)
 problem = minimize(logisticloss(-Y.*(X*beta)))
-solve!(problem, SCSSolver(verbose=false))
+solve!(problem, SCS.Optimizer(verbose=false))
 
 # Let's see how well the model fits.
 using Plots

docs/examples_literate/general_examples/max_entropy.jl

@@ -23,7 +23,7 @@ b = rand(m, 1);
 
 x = Variable(n);
 problem = maximize(entropy(x), sum(x) == 1, A * x <= b)
-solve!(problem, SCSSolver(verbose=false))
+solve!(problem, SCS.Optimizer(verbose=false))
 problem.optval
 
 #-

docs/examples_literate/general_examples/optimal_advertising.jl

@@ -46,7 +46,7 @@ D = Variable(m, n);
 Si = [min(R[i]*dot(P[i,:], D[i,:]'), B[i]) for i=1:m];
 problem = maximize(sum(Si),
                [D >= 0, sum(D, dims=1)' <= T, sum(D, dims=2) >= c]);
-solve!(problem, SCSSolver(verbose=0));
+solve!(problem, SCS.Optimizer(verbose=0));
 
 #-
 

docs/examples_literate/general_examples/robust_approx_fitting.jl

@@ -43,18 +43,18 @@ x = Variable(n)
 
 # Case 1: Nominal optimal solution
 p = minimize(norm(A * x - b, 2))
-solve!(p, SCSSolver(verbose=0))
+solve!(p, SCS.Optimizer(verbose=0))
 x_nom = evaluate(x)
 
 # Case 2: Stochastic robust approximation
 P = 1 / 3 * B' * B;
 p = minimize(square(pos(norm(A * x - b))) + quadform(x, Symmetric(P)))
-solve!(p, SCSSolver(verbose=0))
+solve!(p, SCS.Optimizer(verbose=0))
 x_stoch = evaluate(x)
 
 # Case 3: Worst-case robust approximation
 p = minimize(max(norm((A - B) * x - b), norm((A + B) * x - b)))
-solve!(p, SCSSolver(verbose=0))
+solve!(p, SCS.Optimizer(verbose=0))
 x_wc = evaluate(x)
 
 # Plot residuals:

docs/examples_literate/general_examples/svm.jl

@@ -34,7 +34,7 @@ neg_data = rand(MvNormal([-1.0, 2.0], 1.0), M);
 
 #-
 
-function svm(pos_data, neg_data, solver=SCSSolver(verbose=0))
+function svm(pos_data, neg_data, solver=SCS.Optimizer(verbose=0))
     ## Create variables for the separating hyperplane w'*x = b.
     w = Variable(n)
     b = Variable()

docs/examples_literate/general_examples/svm_l1regularization.jl

@@ -34,8 +34,8 @@ beta_vals = zeros(length(beta), TRIALS);
 for i = 1:TRIALS
     lambda = lambda_vals[i];
     problem = minimize(loss/m + lambda*reg);
-    solve!(problem, ECOSSolver(verbose=0));
-    ## solve!(problem, SCSSolver(verbose=0,linear_solver=SCS.Direct, eps=1e-3))
+    solve!(problem, ECOS.Optimizer(verbose=0));
+    ## solve!(problem, SCS.Optimizer(verbose=0,linear_solver=SCS.Direct, eps=1e-3))
     train_error[i] = sum(float(sign.(X*beta_true .+ offset) .!= sign.(evaluate(X*beta - v))))/m;
     test_error[i] = sum(float(sign.(X_test*beta_true .+ offset) .!= sign.(evaluate(X_test*beta - v))))/TEST;
     beta_vals[:, i] =  evaluate(beta);

docs/examples_literate/general_examples/trade_off_curves.jl

@@ -18,7 +18,7 @@ x = Variable(n);
 for i=1:length(gammas)
     cost = sumsquares(A*x - b) + gammas[i]*norm(x,1);
     problem = minimize(cost, [norm(x, Inf) <= 1]);
-    solve!(problem, SCSSolver(verbose=0));
+    solve!(problem, SCS.Optimizer(verbose=0));
     x_values[:,i] = evaluate(x);
 end
 

docs/examples_literate/general_examples/worst_case_analysis.jl

@@ -18,7 +18,7 @@ w = Variable(n);
 ret = dot(r, w);
 risk = sum(quadform(w, Sigma_nom));
 problem = minimize(risk, [sum(w) == 1, ret >= 0.1, norm(w, 1) <= 2])
-solve!(problem, SCSSolver(verbose=0));
+solve!(problem, SCS.Optimizer(verbose=0));
 wval = vec(evaluate(w))
 
 #-
@@ -31,7 +31,7 @@ problem = maximize(risk, [Sigma == Sigma_nom + Delta,
                     diag(Delta) == 0,
                     abs(Delta) <= 0.2,
                     Delta == Delta']);
-solve!(problem, SCSSolver(verbose=0));
+solve!(problem, SCS.Optimizer(verbose=0));
 println("standard deviation = ", round(sqrt(wval' * Sigma_nom * wval), sigdigits=2));
 println("worst-case standard deviation = ", round(sqrt(evaluate(risk)), sigdigits=2));
 println("worst-case Delta = ");

docs/examples_literate/mixed_integer/binary_knapsack.jl

@@ -21,8 +21,8 @@ n = length(w)
 
 #-
 
-using Convex, GLPKMathProgInterface
+using Convex, GLPK
 x = Variable(n, :Bin)
 problem = maximize(dot(p, x), dot(w, x) <= C)
-solve!(problem, GLPKSolverMIP())
+solve!(problem, GLPK.Optimizer())
 evaluate(x)

docs/examples_literate/mixed_integer/n_queens.jl

@@ -1,6 +1,6 @@
 # # N queens
 
-using Convex, GLPKMathProgInterface, LinearAlgebra, SparseArrays, Test
+using Convex, GLPK, LinearAlgebra, SparseArrays, Test
 aux(str) = joinpath(@__DIR__, "aux", str) # path to auxiliary files
 include(aux("antidiag.jl"))
 
@@ -16,7 +16,7 @@ constr += Constraint[sum(diag(x, k)) <= 1 for k = -n+2:n-2]
 ## Exactly one queen per row and one queen per column
 constr += Constraint[sum(x, dims=1) == 1, sum(x, dims=2) == 1]
 p = satisfy(constr)
-solve!(p, GLPKSolverMIP())
+solve!(p, GLPK.Optimizer())
 
 # Let us test the results:
 for k = -n+2:n-2

docs/examples_literate/mixed_integer/section_allocation.jl

@@ -9,7 +9,7 @@
 # The goal will be to get an allocation matrix $X$, where $X_{ij} = 1$ if
 # student $i$ is assigned to section $j$ and $0$ otherwise. 
 
-using Convex, GLPKMathProgInterface
+using Convex, GLPK
 aux(str) = joinpath(@__DIR__, "aux", str) # path to auxiliary files
 
 # Load our preference matrix, `P`
@@ -30,5 +30,5 @@ constraints = [sum(X, dims=2) == 1, sum(X, dims=1) <= 10, sum(X, dims=1) >= 6]
 # students since the ranking of the first choice is 1.
 p = minimize(vec(X)' * vec(P), constraints)
 
-solve!(p, GLPKSolverMIP())
+solve!(p, GLPK.Optimizer())
 p.optval

docs/examples_literate/optimization_with_complex_variables/Fidelity in Quantum Information Theory.jl

@@ -33,7 +33,7 @@ Z = ComplexVariable(n,n)
 objective = 0.5*real(tr(Z+Z'))
 constraint = [P Z;Z' Q] ⪰ 0
 problem = maximize(objective,constraint)
-solve!(problem, SCSSolver(verbose=0))
+solve!(problem, SCS.Optimizer(verbose=0))
 computed_fidelity = evaluate(objective)
 
 #-

docs/examples_literate/optimization_with_complex_variables/phase_recovery_using_MaxCut.jl

@@ -69,7 +69,7 @@ objective = inner_product(U,M)
 c1 = diag(U) == 1 
 c2 = U in :SDP
 p = minimize(objective,c1,c2)
-solve!(p, SCSSolver(verbose=0))
+solve!(p, SCS.Optimizer(verbose=0))
 U.value
 
 #-

docs/examples_literate/optimization_with_complex_variables/povm_simulation.jl

@@ -55,7 +55,7 @@ function get_visibility(K)
     constraints += t*K[3] + (1-t)*noise[3] == P[2][2] + P[4][2] + P[6][1]
     constraints += t*K[4] + (1-t)*noise[4] == P[3][2] + P[5][2] + P[6][2]
     p = maximize(t, constraints)
-    solve!(p, SCSSolver(verbose=0))
+    solve!(p, SCS.Optimizer(verbose=0))
     return p.optval
 end
 

docs/examples_literate/optimization_with_complex_variables/power_flow_optimization.jl

@@ -25,7 +25,7 @@ c3 = real(W[1,1]) == 1.06^2;
 push!(c1, c2)
 push!(c1, c3)
 p = maximize(objective, c1);
-solve!(p, SCSSolver(verbose = 0))
+solve!(p, SCS.Optimizer(verbose = 0))
 p.optval
 #15.125857662600703
 evaluate(objective)

docs/examples_literate/portfolio_optimization/portfolio_optimization.jl

@@ -56,7 +56,7 @@ p = minimize( risk,
               w_lower <= w, 
               w <= w_upper )
 
-solve!(p, SCSSolver())     #use SCSSolver(verbose = false) to suppress printing
+solve!(p, SCS.Optimizer())     #use SCS.Optimizer(verbose = false) to suppress printing
 
 #-
 

docs/examples_literate/portfolio_optimization/portfolio_optimization2.jl

@@ -52,7 +52,7 @@ for i = 1:N
     λ = λ_vals[i]
     p = minimize( λ*risk - (1-λ)*ret,
                   sum(w) == 1 )    
-    solve!(p, SCSSolver(verbose = false))
+    solve!(p, SCS.Optimizer(verbose = false))
     MeanVarA[i,:]= [evaluate(ret),evaluate(risk)[1]]    #risk is a 1x1 matrix
 end
 
@@ -68,7 +68,7 @@ for i = 1:N
                   sum(w) == 1,
                   w_lower <= w,     #w[i] is bounded
                   w <= w_upper )
-    solve!(p, SCSSolver(verbose = false))
+    solve!(p, SCS.Optimizer(verbose = false))
     MeanVarB[i,:]= [evaluate(ret),evaluate(risk)[1]]    
 end
 
@@ -95,7 +95,7 @@ for i = 1:N
     p = minimize( λ*risk - (1-λ)*ret,
                   sum(w) == 1,
                   (norm(w, 1)-1) <= Lmax)
-    solve!(p, SCSSolver(verbose = false))
+    solve!(p, SCS.Optimizer(verbose = false))
     MeanVarC[i,:]= [evaluate(ret),evaluate(risk)[1]]    
 end