Deprecate silent_solver to silent (#670)

ericphanson · web-flow · commit ecc91cd1f324 · 2024-05-20T09:12:58.000+12:00
diff --git a/docs/src/examples/general_examples/basic_usage.jl b/docs/src/examples/general_examples/basic_usage.jl
@@ -26,7 +26,7 @@ A = I(4)
 b = [10; 10; 10; 10]
 constraints = [A * x <= b, x >= 1, x <= 10, x[2] <= 5, x[1] + x[4] - x[2] <= 10]
 p = minimize(dot(c, x), constraints) # or c' * x
-solve!(p, SCS.Optimizer; silent_solver = true)
+solve!(p, SCS.Optimizer; silent = true)
 
 # We can also inspect the objective value and the values of the variables at the solution:
 println(round(p.optval, digits = 2))
@@ -50,7 +50,7 @@ X = Variable(2, 2)
 y = Variable()
 ## X is a 2 x 2 variable, and y is scalar. X' + y promotes y to a 2 x 2 variable before adding them
 p = minimize(norm(X) + y, 2 * X <= 1, X' + y >= 1, X >= 0, y >= 0)
-solve!(p, SCS.Optimizer; silent_solver = true)
+solve!(p, SCS.Optimizer; silent = true)
 
 # We can also inspect the values of the variables at the solution:
 println(round.(evaluate(X), digits = 2))
@@ -76,13 +76,13 @@ p = satisfy(
     x[2] >= 7,
     geomean(x[3], x[4]) >= x[2],
 )
-solve!(p, SCS.Optimizer; silent_solver = true)
+solve!(p, SCS.Optimizer; silent = true)
 
 # ### PSD cone and Eigenvalues
 
 y = Semidefinite(2)
 p = maximize(eigmin(y), tr(y) <= 6)
-solve!(p, SCS.Optimizer; silent_solver = true)
+solve!(p, SCS.Optimizer; silent = true)
 
 #-
 
@@ -91,7 +91,7 @@ y = Variable((2, 2))
 
 ## PSD constraints
 p = minimize(x + y[1, 1], y ⪰ 0, x >= 1, y[2, 1] == 1)
-solve!(p, SCS.Optimizer; silent_solver = true)
+solve!(p, SCS.Optimizer; silent = true)
 
 # ### Mixed integer program
 #
@@ -106,7 +106,7 @@ solve!(p, SCS.Optimizer; silent_solver = true)
 
 x = Variable(4, IntVar)
 p = minimize(sum(x), x >= 0.5)
-solve!(p, GLPK.Optimizer; silent_solver = true)
+solve!(p, GLPK.Optimizer; silent = true)
 
 # And the value of `x` at the solution:
 evaluate(x)
diff --git a/docs/src/examples/general_examples/chebyshev_center.jl b/docs/src/examples/general_examples/chebyshev_center.jl
@@ -29,7 +29,7 @@ constraints = [
     a4' * x_c + r * norm(a4, 2) <= b[4],
 ]
 p = maximize(r, constraints)
-solve!(p, SCS.Optimizer; silent_solver = true)
+solve!(p, SCS.Optimizer; silent = true)
 
 # Generate the figure
 x = range(-1.5, stop = 1.5, length = 100);
diff --git a/docs/src/examples/general_examples/control.jl b/docs/src/examples/general_examples/control.jl
@@ -121,7 +121,7 @@ push!(constraints, velocity[:, T] == 0)
 
 ## Solve the problem
 problem = minimize(sumsquares(force), constraints)
-solve!(problem, SCS.Optimizer; silent_solver = true)
+solve!(problem, SCS.Optimizer; silent = true)
 
 # We can plot the trajectory taken by the object.
 
diff --git a/docs/src/examples/general_examples/dualization.jl b/docs/src/examples/general_examples/dualization.jl
@@ -25,8 +25,8 @@ p = 50
 # Now we formulate and solve our primal problem:
 d = Variable(p)
 problem = maximize(sum(d), 0 ≤ d, d ≤ 1, Σ ⪰ Diagonal(d))
-@time solve!(problem, SCS.Optimizer; silent_solver = true)
+@time solve!(problem, SCS.Optimizer; silent = true)
 
 # To solve the dual problem instead, we simply call `dual_optimizer` on our
 # optimizer function:
-@time solve!(problem, dual_optimizer(SCS.Optimizer); silent_solver = true)
+@time solve!(problem, dual_optimizer(SCS.Optimizer); silent = true)
diff --git a/docs/src/examples/general_examples/huber_regression.jl b/docs/src/examples/general_examples/huber_regression.jl
@@ -40,18 +40,18 @@ for i in 1:length(p_vals)
     fit = norm(beta - beta_true) / norm(beta_true)
     cost = norm(X' * beta - Y)
     prob = minimize(cost)
-    solve!(prob, SCS.Optimizer; silent_solver = true)
+    solve!(prob, SCS.Optimizer; silent = true)
     lsq_data[i] = evaluate(fit)
 
     ## Form and solve a prescient regression problem,
     ## that is, where the sign changes are known.
     cost = norm(factor .* (X' * beta) - Y)
-    solve!(minimize(cost), SCS.Optimizer; silent_solver = true)
+    solve!(minimize(cost), SCS.Optimizer; silent = true)
     prescient_data[i] = evaluate(fit)
 
     ## Form and solve the Huber regression problem.
     cost = sum(huber(X' * beta - Y, 1))
-    solve!(minimize(cost), SCS.Optimizer; silent_solver = true)
+    solve!(minimize(cost), SCS.Optimizer; silent = true)
     huber_data[i] = evaluate(fit)
 end
 
diff --git a/docs/src/examples/general_examples/lasso_regression.jl b/docs/src/examples/general_examples/lasso_regression.jl
@@ -66,7 +66,7 @@ function LassoEN(Y, X, γ, λ = 0)
         ## u'u/T + γ*sum(|b|) where u = Y-Xb
         problem = minimize(L1 - 2 * L2 + γ * L3)
     end
-    solve!(problem, SCS.Optimizer; silent_solver = true)
+    solve!(problem, SCS.Optimizer; silent = true)
     problem.status == Convex.MOI.OPTIMAL ? b_i = vec(evaluate(b)) : b_i = NaN
 
     return b_i, b_ls
diff --git a/docs/src/examples/general_examples/logistic_regression.jl b/docs/src/examples/general_examples/logistic_regression.jl
@@ -28,7 +28,7 @@ X = hcat(
 n, p = size(X)
 beta = Variable(p)
 problem = minimize(logisticloss(-Y .* (X * beta)))
-solve!(problem, SCS.Optimizer; silent_solver = true)
+solve!(problem, SCS.Optimizer; silent = true)
 
 # Let's see how well the model fits.
 using Plots
diff --git a/docs/src/examples/general_examples/max_entropy.jl b/docs/src/examples/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, SCS.Optimizer; silent_solver = true)
+solve!(problem, SCS.Optimizer; silent = true)
 
 #-
 
diff --git a/docs/src/examples/general_examples/optimal_advertising.jl b/docs/src/examples/general_examples/optimal_advertising.jl
@@ -45,7 +45,7 @@ D = Variable(m, n);
 Si = [min(R[i] * dot(P[i, :], D[i, :]'), B[i]) for i in 1:m];
 problem =
     maximize(sum(Si), [D >= 0, sum(D, dims = 1)' <= T, sum(D, dims = 2) >= c]);
-solve!(problem, SCS.Optimizer; silent_solver = true)
+solve!(problem, SCS.Optimizer; silent = true)
 
 #-
 
diff --git a/docs/src/examples/general_examples/robust_approx_fitting.jl b/docs/src/examples/general_examples/robust_approx_fitting.jl
@@ -41,20 +41,20 @@ x = Variable(n)
 
 # ## Case 1: nominal optimal solution
 p = minimize(norm(A * x - b, 2))
-solve!(p, SCS.Optimizer; silent_solver = true)
+solve!(p, SCS.Optimizer; silent = true)
 #-
 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; silent_solver = true)
+solve!(p, SCS.Optimizer; silent = true)
 #-
 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; silent_solver = true)
+solve!(p, SCS.Optimizer; silent = true)
 
 #-
 x_wc = evaluate(x)
diff --git a/docs/src/examples/general_examples/svm.jl b/docs/src/examples/general_examples/svm.jl
@@ -45,7 +45,7 @@ function svm(pos_data, neg_data)
         C * sum(max(1 - b + w' * neg_data, 0))
     ## Form and solve problem.
     problem = minimize(obj)
-    solve!(problem, SCS.Optimizer; silent_solver = true)
+    solve!(problem, SCS.Optimizer; silent = true)
     return evaluate(w), evaluate(b)
 end;
 
diff --git a/docs/src/examples/general_examples/svm_l1regularization.jl b/docs/src/examples/general_examples/svm_l1regularization.jl
@@ -34,7 +34,7 @@ beta_vals = zeros(length(beta), TRIALS);
 for i in 1:TRIALS
     lambda = lambda_vals[i]
     problem = minimize(loss / m + lambda * reg)
-    solve!(problem, SCS.Optimizer; silent_solver = true)
+    solve!(problem, SCS.Optimizer; silent = true)
     train_error[i] =
         sum(
             float(
diff --git a/docs/src/examples/general_examples/trade_off_curves.jl b/docs/src/examples/general_examples/trade_off_curves.jl
@@ -19,7 +19,7 @@ x = Variable(n);
 for i in 1:length(gammas)
     cost = sumsquares(A * x - b) + gammas[i] * norm(x, 1)
     problem = minimize(cost, [norm(x, Inf) <= 1])
-    solve!(problem, SCS.Optimizer; silent_solver = true)
+    solve!(problem, SCS.Optimizer; silent = true)
     x_values[:, i] = evaluate(x)
 end
 
diff --git a/docs/src/examples/general_examples/worst_case_analysis.jl b/docs/src/examples/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; silent_solver = true)
+solve!(problem, SCS.Optimizer; silent = true)
 #-
 wval = vec(evaluate(w))
 
@@ -37,7 +37,7 @@ problem = maximize(
         Delta == Delta',
     ],
 );
-solve!(problem, SCS.Optimizer; silent_solver = true)
+solve!(problem, SCS.Optimizer; silent = true)
 #-
 println(
     "standard deviation = ",
diff --git a/docs/src/examples/optimization_with_complex_variables/Fidelity in Quantum Information Theory.jl b/docs/src/examples/optimization_with_complex_variables/Fidelity in Quantum Information Theory.jl
@@ -30,7 +30,7 @@ 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; silent_solver = true)
+solve!(problem, SCS.Optimizer; silent = true)
 #-
 computed_fidelity = evaluate(objective)
 
diff --git a/docs/src/examples/optimization_with_complex_variables/phase_recovery_using_MaxCut.jl b/docs/src/examples/optimization_with_complex_variables/phase_recovery_using_MaxCut.jl
@@ -65,7 +65,7 @@ objective = inner_product(U, M)
 c1 = diag(U) == 1
 c2 = isposdef(U)
 p = minimize(objective, c1, c2)
-solve!(p, SCS.Optimizer; silent_solver = true)
+solve!(p, SCS.Optimizer; silent = true)
 #-
 
 evaluate(U)
diff --git a/docs/src/examples/optimization_with_complex_variables/povm_simulation.jl b/docs/src/examples/optimization_with_complex_variables/povm_simulation.jl
@@ -63,7 +63,7 @@ function get_visibility(K)
         t * K[4] + (1 - t) * noise[4] == P[3][2] + P[5][2] + P[6][2],
     )
     p = maximize(t, constraints)
-    return solve!(p, SCS.Optimizer; silent_solver = true)
+    return solve!(p, SCS.Optimizer; silent = true)
 end
 
 # We check this function using the tetrahedron measurement (see Appendix B in [arXiv:quant-ph/0702021](https://arxiv.org/abs/quant-ph/0702021)). This measurement is non-simulable, so we expect a value below one.
diff --git a/docs/src/examples/optimization_with_complex_variables/power_flow_optimization.jl b/docs/src/examples/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, SCS.Optimizer; silent_solver = true)
+solve!(p, SCS.Optimizer; silent = true)
 #-
 p.optval
 
diff --git a/docs/src/examples/portfolio_optimization/portfolio_optimization2.jl b/docs/src/examples/portfolio_optimization/portfolio_optimization2.jl
@@ -53,7 +53,7 @@ MeanVarA = zeros(N, 2)
 for i in 1:N
     λ = λ_vals[i]
     p = minimize(λ * risk - (1 - λ) * ret, sum(w) == 1)
-    solve!(p, SCS.Optimizer; silent_solver = true)
+    solve!(p, SCS.Optimizer; silent = true)
     MeanVarA[i, :] = [evaluate(ret), evaluate(risk)]
 end
 
@@ -71,7 +71,7 @@ for i in 1:N
         w_lower <= w,     #w[i] is bounded
         w <= w_upper,
     )
-    solve!(p, SCS.Optimizer; silent_solver = true)
+    solve!(p, SCS.Optimizer; silent = true)
     MeanVarB[i, :] = [evaluate(ret), evaluate(risk)]
 end
 
@@ -102,7 +102,7 @@ for i in 1:N
         sum(w) == 1,
         (norm(w, 1) - 1) <= Lmax,
     )
-    solve!(p, SCS.Optimizer; silent_solver = true)
+    solve!(p, SCS.Optimizer; silent = true)
     MeanVarC[i, :] = [evaluate(ret), evaluate(risk)]
 end
 
diff --git a/docs/src/examples/supplemental_material/Convex.jl_intro_ISMP2015.jl b/docs/src/examples/supplemental_material/Convex.jl_intro_ISMP2015.jl
@@ -44,7 +44,7 @@ problem = minimize(
 )
 
 # Solve the problem by calling `solve!`
-solve!(problem, SCS.Optimizer; silent_solver = true)
+solve!(problem, SCS.Optimizer; silent = true)
 
 println("problem status is ", problem.status)
 println("optimal value is ", problem.optval)
@@ -59,7 +59,7 @@ using Interact, Plots
         square(norm(A * x - b)) + λ * square(norm(x)) + μ * norm(x, 1),
         x >= 0,
     )
-    solve!(problem, SCS.Optimizer; silent_solver = true)
+    solve!(problem, SCS.Optimizer; silent = true)
     histogram(evaluate(x), xlims = (0, 3.5), label = "x")
 end
 nothing # hide
@@ -134,7 +134,7 @@ p = minimize(objective, constraint)
 #-
 
 # Solve the problem:
-solve!(p, SCS.Optimizer; silent_solver = true)
+solve!(p, SCS.Optimizer; silent = true)
 p.status
 
 #-
@@ -182,9 +182,9 @@ problem = minimize(
     square(norm(A * x - b)) + λ * square(norm(x)) + μ * norm(x, 1),
     x >= 0,
 )
-@time solve!(problem, SCS.Optimizer; silent_solver = true)
+@time solve!(problem, SCS.Optimizer; silent = true)
 λ = 1.5
-@time solve!(problem, SCS.Optimizer; silent_solver = true)#, warmstart = true) # FIXME
+@time solve!(problem, SCS.Optimizer; silent = true)#, warmstart = true) # FIXME
 
 # # DCP examples
 
diff --git a/docs/src/examples/supplemental_material/paper_examples.jl b/docs/src/examples/supplemental_material/paper_examples.jl
@@ -13,7 +13,7 @@ e = 0;
     end
     p = minimize(e, x >= 1)
 end
-@time solve!(p, ECOS.Optimizer; silent_solver = true)
+@time solve!(p, ECOS.Optimizer; silent = true)
 
 # Indexing.
 println("Indexing example")
@@ -26,7 +26,7 @@ e = 0;
     end
     p = minimize(e, x >= ones(1000, 1))
 end
-@time solve!(p, ECOS.Optimizer; silent_solver = true)
+@time solve!(p, ECOS.Optimizer; silent = true)
 
 # Matrix constraints.
 println("Matrix constraint example")
@@ -37,7 +37,7 @@ b = randn(p, n);
 @time begin
     p = minimize(norm(X), A * X == b)
 end
-@time solve!(p, ECOS.Optimizer; silent_solver = true)
+@time solve!(p, ECOS.Optimizer; silent = true)
 
 # Transpose.
 println("Transpose example")
@@ -46,12 +46,12 @@ A = randn(5, 5);
 @time begin
     p = minimize(norm2(X - A), X' == X)
 end
-@time solve!(p, ECOS.Optimizer; silent_solver = true)
+@time solve!(p, ECOS.Optimizer; silent = true)
 
 n = 3
 A = randn(n, n);
 #@time begin
 X = Variable(n, n);
 p = minimize(norm(X' - A), X[1, 1] == 1);
-solve!(p, ECOS.Optimizer; silent_solver = true)
+solve!(p, ECOS.Optimizer; silent = true)
 #end
diff --git a/docs/src/examples/tomography/tomography.jl b/docs/src/examples/tomography/tomography.jl
@@ -62,7 +62,7 @@ pixel_colors = Variable(num_pixels)
 ## to reflect that, we minimize a norm
 objective = sumsquares(line_mat * pixel_colors - line_vals)
 problem = minimize(objective)
-solve!(problem, ECOS.Optimizer; silent_solver = true)
+solve!(problem, ECOS.Optimizer; silent = true)
 
 #-
 
diff --git a/docs/src/introduction/dcp.md b/docs/src/introduction/dcp.md
@@ -22,7 +22,7 @@ As a simple example, consider the problem:
 using Convex, SCS
 x = Variable();
 model_min = minimize(abs(x), [x >= 1, x <= 2]);
-solve!(model_min, SCS.Optimizer; silent_solver = true)
+solve!(model_min, SCS.Optimizer; silent = true)
 x.value
 ```
 
@@ -37,7 +37,7 @@ using Convex, SCS
 x = Variable();
 t = Variable();
 model_min_extended = minimize(t, [x >= 1, x <= 2, t >= x, t >= -x]);
-solve!(model_min_extended, SCS.Optimizer; silent_solver = true)
+solve!(model_min_extended, SCS.Optimizer; silent = true)
 x.value
 ```
 That is, we add the constraints `t >= x` and `t >= -x`, and replace `abs(x)` by
@@ -60,7 +60,7 @@ model_max = maximize(abs(x), [x >= 1, x <= 2])
 This time, `problem is DCP` reports `false`. If we attempt to solve the problem,
 an error is thrown:
 ```julia
-julia> solve!(model_max, SCS.Optimizer; silent_solver = true)
+julia> solve!(model_max, SCS.Optimizer; silent = true)
 ┌ Warning: Problem not DCP compliant: objective is not DCP
 └ @ Convex ~/.julia/dev/Convex/src/problems.jl:73
 ERROR: DCPViolationError: Expression not DCP compliant. This either means that your problem is not convex, or that we could not prove it was convex using the rules of disciplined convex programming. For a list of supported operations, see https://jump.dev/Convex.jl/stable/operations/. For help writing your problem as a disciplined convex program, please post a reproducible example on https://jump.dev/forum.
@@ -75,7 +75,7 @@ using Convex, SCS
 x = Variable();
 t = Variable();
 model_max_extended = maximize(t, [x >= 1, x <= 2, t >= x, t >= -x]);
-solve!(model_max_extended, SCS.Optimizer; silent_solver = true)
+solve!(model_max_extended, SCS.Optimizer; silent = true)
 ```
 whose solution is unbounded.
 
diff --git a/docs/src/introduction/quick_tutorial.md b/docs/src/introduction/quick_tutorial.md
diff --git a/docs/src/manual/advanced.md b/docs/src/manual/advanced.md
diff --git a/docs/src/manual/complex-domain_optimization.md b/docs/src/manual/complex-domain_optimization.md
diff --git a/docs/src/manual/solvers.md b/docs/src/manual/solvers.md
diff --git a/src/problem_depot/problem_depot.jl b/src/problem_depot/problem_depot.jl
diff --git a/src/solution.jl b/src/solution.jl
diff --git a/test/test_constraints.jl b/test/test_constraints.jl
diff --git a/test/test_problem_depot.jl b/test/test_problem_depot.jl
diff --git a/test/test_utilities.jl b/test/test_utilities.jl

Original file line number	Diff line number	Diff line change
`@@ -29,7 +29,7 @@ constraints = [`
`29`	`29`	`a4' * x_c + r * norm(a4, 2) <= b[4],`
`30`	`30`	`]`
`31`	`31`	`p = maximize(r, constraints)`
`32`		`-solve!(p, SCS.Optimizer; silent_solver = true)`
	`32`	`+solve!(p, SCS.Optimizer; silent = true)`
`33`	`33`
`34`	`34`	`# Generate the figure`
`35`	`35`	`x = range(-1.5, stop = 1.5, length = 100);`