Update to MOI v1.0

.github/workflows/ci.yml

@@ -18,17 +18,14 @@ on:
       - 'Project.toml'
 jobs:
   test:
-    name: Julia ${{ matrix.version }} - MOI ${{ matrix.moi-version }} - ${{ matrix.os }} - ${{ matrix.arch }} - ${{ github.event_name }}
+    name: Julia ${{ matrix.version }} - ${{ matrix.os }} - ${{ matrix.arch }} - ${{ github.event_name }}
     runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false
       matrix:
-        moi-version:
-          - '0.9'
-          - '0.10'
         version:
           - '1'
-          - '1.0'
+          - '1.6'
         os:
           - ubuntu-latest
           - macOS-latest
@@ -40,11 +37,6 @@ jobs:
         with:
           version: ${{ matrix.version }}
           arch: ${{ matrix.arch }}
-      - name: "Install MOI version"
-        shell: julia --color=yes --project=. {0}
-        run: |
-          using Pkg
-          Pkg.add(Pkg.PackageSpec(; name="MathOptInterface", version="${{ matrix.moi-version }}"))
       - uses: actions/cache@v1
         env:
           cache-name: cache-artifacts

.github/workflows/docs.yml

@@ -20,8 +20,8 @@ jobs:
       - uses: actions/checkout@v2
       - uses: julia-actions/setup-julia@latest
         with:
-          # Build documentation on Julia 1.0
-          version: '1.0'
+          # Build documentation on Julia LTS
+          version: '1.6'
       - name: Install dependencies
         run: julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
       - name: Build and deploy

Project.toml

@@ -1,6 +1,6 @@
 name = "Convex"
 uuid = "f65535da-76fb-5f13-bab9-19810c17039a"
-version = "0.14.18"
+version = "0.15.0-DEV"
 
 [deps]
 AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
@@ -14,20 +14,21 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]
 AbstractTrees = "0.2, 0.3"
-BenchmarkTools = "^0.4, 0.5, 0.6, 0.7, 1.0"
+BenchmarkTools = "1"
+ECOS = "1"
+GLPK = "1"
 LDLFactorizations = "0.8.1"
-MathOptInterface = "0.9, 0.10"
-OrderedCollections = "^1.0"
-julia = "^1.0"
+MathOptInterface = "1"
+OrderedCollections = "1"
+SCS = "1"
+julia = "1.6"
 
 [extras]
 ECOS = "e2685f51-7e38-5353-a97d-a921fd2c8199"
 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", "GLPK", "LinearAlgebra", "Random", "SCS", "Statistics", "Test"]
+test = ["ECOS", "GLPK", "Random", "SCS", "Statistics"]

docs/Project.toml

@@ -21,4 +21,4 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]
 Documenter = "0.27"
-MathOptInterface = "0.10"
+MathOptInterface = "1"

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 = () -> SCS.Optimizer(verbose=0)
+solver = MOI.OptimizerWithAttributes(SCS.Optimizer, "verbose" => 0)
 
 # ### Linear program
 #

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, () -> SCS.Optimizer(verbose=0))
+solve!(p, MOI.OptimizerWithAttributes(SCS.Optimizer, "verbose" => 0))
 p.optval
 
 # Generate the figure

docs/examples_literate/general_examples/control.jl

@@ -121,14 +121,14 @@ push!(constraints, velocity[:, T] == 0)
 
 ## Solve the problem
 problem = minimize(sumsquares(force), constraints)
-solve!(problem, () -> SCS.Optimizer(verbose=0))
+solve!(problem, MOI.OptimizerWithAttributes(SCS.Optimizer, "verbose" => 0))
 
 # We can plot the trajectory taken by the object.
 
 pos = evaluate(position)
 plot([pos[1, 1]], [pos[2, 1]], st=:scatter, label="initial point")
 plot!([pos[1, T]], [pos[2, T]], st=:scatter, label="final point")
-plot!(pos[1, :], pos[2, :], label="trajectory") 
+plot!(pos[1, :], pos[2, :], label="trajectory")
 
 # We can also see how the magnitude of the force changes over time.
 

docs/examples_literate/general_examples/huber_regression.jl

@@ -34,24 +34,24 @@ for i=1:length(p_vals)
     ## Generate the sign changes.
     factor = 2 * rand(Binomial(1, 1-p), number_samples) .- 1;
     Y = factor .* X' * beta_true + v;
-    
+
     ## Form and solve a standard regression problem.
     beta = Variable(n);
     fit = norm(beta - beta_true) / norm(beta_true);
     cost = norm(X' * beta - Y);
     prob = minimize(cost);
-    solve!(prob, () -> SCS.Optimizer(verbose=0));
+    solve!(prob, MOI.OptimizerWithAttributes(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), () -> SCS.Optimizer(verbose=0))
+    solve!(minimize(cost), MOI.OptimizerWithAttributes(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), () -> SCS.Optimizer(verbose=0))
+    solve!(minimize(cost), MOI.OptimizerWithAttributes(SCS.Optimizer, "verbose" => 0))
     huber_data[i] = evaluate(fit);
 end
 

docs/examples_literate/general_examples/lasso_regression.jl

@@ -67,7 +67,7 @@ function LassoEN(Y,X,γ,λ=0.0)
     L4 = sumsquares(b)            #sum(b^2)
 
     Sol = minimize(L1-2*L2+γ*L3+λ*L4)      #u'u + γ*sum(|b|) + λsum(b^2), where u = Y-Xb
-    solve!(Sol,()->SCS.Optimizer(verbose = false))
+    solve!(Sol, SCS.Optimizer)
     Sol.status == MOI.OPTIMAL ? b_i = vec(evaluate(b)) : b_i = NaN
 
     return b_i, b_ls

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, () -> SCS.Optimizer(verbose=false))
+solve!(problem, MOI.OptimizerWithAttributes(SCS.Optimizer, "verbose" => 0))
 
 # Let's see how well the model fits.
 using Plots

docs/examples_literate/general_examples/max_entropy.jl

@@ -18,12 +18,12 @@ using Convex, SCS
 
 n = 25;
 m = 15;
-A = randn(m, n); 
-b = rand(m, 1); 
+A = randn(m, n);
+b = rand(m, 1);
 
 x = Variable(n);
 problem = maximize(entropy(x), sum(x) == 1, A * x <= b)
-solve!(problem, () -> SCS.Optimizer(verbose=false))
+solve!(problem, MOI.OptimizerWithAttributes(SCS.Optimizer, "verbose" => 0))
 problem.optval
 
 #-

docs/examples_literate/general_examples/optimal_advertising.jl

@@ -27,8 +27,8 @@ SCALE = 10000;
 B = rand(LogNormal(8), m) .+ 10000;
 B = round.(B, digits=3); # Budget
 
-P_ad = rand(m); 
-P_time = rand(1,n); 
+P_ad = rand(m);
+P_time = rand(1,n);
 P = P_ad * P_time;
 
 T = sin.(range(-2*pi/2, stop=2*pi-2*pi/2, length=n)) * SCALE;
@@ -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, () -> SCS.Optimizer(verbose=0));
+solve!(problem, MOI.OptimizerWithAttributes(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, () -> SCS.Optimizer(verbose=0))
+solve!(p, MOI.OptimizerWithAttributes(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, () -> SCS.Optimizer(verbose=0))
+solve!(p, MOI.OptimizerWithAttributes(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, () -> SCS.Optimizer(verbose=0))
+solve!(p, MOI.OptimizerWithAttributes(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=() -> SCS.Optimizer(verbose=0))
+function svm(pos_data, neg_data, solver=MOI.OptimizerWithAttributes(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 +34,7 @@ beta_vals = zeros(length(beta), TRIALS);
 for i = 1:TRIALS
     lambda = lambda_vals[i];
     problem = minimize(loss/m + lambda*reg);
-    solve!(problem, () -> ECOS.Optimizer(verbose=0));
-    ## solve!(problem, SCS.Optimizer(verbose=0,linear_solver=SCS.Direct, eps=1e-3))
+    solve!(problem, MOI.OptimizerWIthAttributes(ECOS.Optimizer, "verbose" => 0));
     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, () -> SCS.Optimizer(verbose=0));
+    solve!(problem, MOI.OptimizerWithAttributes(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, () -> SCS.Optimizer(verbose=0));
+solve!(problem, MOI.OptimizerWithAttributes(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, () -> SCS.Optimizer(verbose=0));
+solve!(problem, MOI.OptimizerWithAttributes(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/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, () -> SCS.Optimizer(verbose=0))
+solve!(problem, MOI.OptimizerWithAttributes(SCS.Optimizer, "verbose" => 0))
 computed_fidelity = evaluate(objective)
 
 #-

docs/examples_literate/optimization_with_complex_variables/phase_recovery_using_MaxCut.jl

@@ -1,5 +1,5 @@
 # # Phase recovery using MaxCut
-# 
+#
 # In this example, we relax the phase retrieval problem similar to the classical
 # [MaxCut](http://www-math.mit.edu/~goemans/PAPERS/maxcut-jacm.pdf) semidefinite
 # program and recover the phase of the signal given the magnitude of the linear
@@ -29,7 +29,7 @@
 # Given a linear operator $A$ and a vector $b= |Ax|$ of measured amplitudes,
 # in the noiseless case, we can write $Ax = \text{diag}(b)u$ where
 # $u \in \mathbb{C}^n$  is a phase vector, satisfying
-# $|\mathbb{u}_i| = 1$ for $i = 1,\ldots, n$. 
+# $|\mathbb{u}_i| = 1$ for $i = 1,\ldots, n$.
 #
 # We relax this problem as Complex Semidefinite Programming.
 #
@@ -66,10 +66,10 @@ b = abs.(A*x) + rand(n)
 M = diagm(b)*(I(n)-A*A')*diagm(b)
 U = ComplexVariable(n,n)
 objective = inner_product(U,M)
-c1 = diag(U) == 1 
+c1 = diag(U) == 1
 c2 = U in :SDP
 p = minimize(objective,c1,c2)
-solve!(p, () -> SCS.Optimizer(verbose=0))
+solve!(p, MOI.OptimizerWithAttributes(SCS.Optimizer, "verbose" => 0))
 evaluate(U)
 
 
@@ -79,7 +79,7 @@ evaluate(U)
 B, C = eigen(evaluate(U));
 length([e for e in B if(abs(real(e))>1e-4)])
 
-#- 
+#-
 
 # Decompose $U = uu^*$ where $u$ is the phase of $Ax$
 u = C[:,1];

docs/examples_literate/optimization_with_complex_variables/povm_simulation.jl

@@ -15,7 +15,7 @@ if VERSION < v"1.2.0-DEV.0"
      LinearAlgebra.diagm(v::AbstractVector) = diagm(0 => v)
 end
 
-# For the qubit case, a four outcome qubit POVM $\mathbf{M} \in\mathcal{P}(2,4)$ is simulable if and only if 
+# For the qubit case, a four outcome qubit POVM $\mathbf{M} \in\mathcal{P}(2,4)$ is simulable if and only if
 #
 # $M_{1}=N_{12}^{+}+N_{13}^{+}+N_{14}^{+},$
 #
@@ -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, () -> SCS.Optimizer(verbose=0))
+    solve!(p, MOI.OptimizerWithAttributes(SCS.Optimizer, "verbose" => 0))
     return p.optval
 end
 
@@ -64,9 +64,9 @@ end
 function dp(v)
     I(2) + v[1]*[0 1; 1 0] + v[2]*[0 -im; im 0] + v[3]*[1 0; 0 -1]
 end
-b = [ 1  1  1; 
-     -1 -1  1; 
-     -1  1 -1;  
+b = [ 1  1  1;
+     -1 -1  1;
+     -1  1 -1;
       1 -1 -1]/sqrt(3)
 M = [dp(b[i, :]) for i=1:size(b,1)]/4;
 get_visibility(M)

docs/examples_literate/optimization_with_complex_variables/power_flow_optimization.jl

@@ -1,5 +1,5 @@
 # # Power flow optimization
-# The data for example is taken from [MATPOWER](http://www.pserc.cornell.edu/matpower/) website. MATPOWER is Matlab package for solving power flow and optimal power flow problems.  
+# The data for example is taken from [MATPOWER](http://www.pserc.cornell.edu/matpower/) website. MATPOWER is Matlab package for solving power flow and optimal power flow problems.
 
 using Convex, SCS
 using Test
@@ -25,7 +25,7 @@ c3 = real(W[1,1]) == 1.06^2;
 push!(c1, c2)
 push!(c1, c3)
 p = maximize(objective, c1);
-solve!(p, () -> SCS.Optimizer(verbose = 0))
+solve!(p, MOI.OptimizerWithAttributes(SCS.Optimizer, "verbose" => 0))
 p.optval
 #15.125857662600703
 evaluate(objective)

docs/examples_literate/portfolio_optimization/portfolio_optimization.jl

@@ -25,7 +25,7 @@ using Convex, SCS
        34  64   4;
        58   4 100]/100^2
 
-n = length(μ)                   #number of assets 
+n = length(μ)                   #number of assets
 
 R_target = 0.1
 w_lower = 0
@@ -50,13 +50,13 @@ w    = Variable(n)
 ret  = dot(w,μ)
 risk = quadform(w,Σ)
 
-p = minimize( risk, 
-              ret >= R_target, 
-              sum(w) == 1, 
-              w_lower <= w, 
+p = minimize( risk,
+              ret >= R_target,
+              sum(w) == 1,
+              w_lower <= w,
               w <= w_upper )
 
-solve!(p, () -> SCS.Optimizer())     #use SCS.Optimizer(verbose = false) to suppress printing
+solve!(p, SCS.Optimizer)
 
 #-