Add silent_solver keyword argument and tweak CI (#404)

* Add `silent_solver` keyword argument

* Rm benchmark stage from Travis

* Bump CI version

Author: ericphanson

.travis.yml

@@ -5,29 +5,11 @@ os:
   - osx
 julia:
   - 1.0
-  - 1.4
+  - 1.5
 if: branch = master OR tag IS present OR type = pull_request
 notifications:
   email: false
 jobs:
-  include:
-    - name: "Benchmark"
-      julia: 1.4
-      os: linux
-      before_script:
-        - git fetch origin '+refs/heads/master:refs/remotes/origin/master'
-        - git branch baseline origin/master
-        # Run benchmark outside `script` so that it's hidden by default:
-        - julia --project=benchmark -e '
-              using Pkg; Pkg.instantiate();
-              include("benchmark/runjudge.jl");'
-
-      script:
-        - julia --project=benchmark -e '
-              using Pkg; Pkg.instantiate();
-              include("benchmark/pprintjudge.jl");'
-      after_success: skip
-      if: type = pull_request
     - stage: Documentation
       julia: 1.0
       script: julia --project=docs -e '

NEWS.md

@@ -1,7 +1,11 @@
+# Changes in v0.13.7
+
+* fix [#403](https://github.com/jump-dev/Convex.jl/issues/403) by adding the keyword argument `silent_solver` to `solve!`.
+
 # Changes in v0.13.6
 
 * fix [#401](https://github.com/jump-dev/Convex.jl/issues/401) by allowing `diagm(x)`.
-* 
+
 # Changes in v0.13.5
 
 * fix [#398](https://github.com/jump-dev/Convex.jl/issues/398) by allowing `fix!`'d variables in `quadform`.

Project.toml

@@ -1,6 +1,6 @@
 name = "Convex"
 uuid = "f65535da-76fb-5f13-bab9-19810c17039a"
-version = "0.13.6"
+version = "0.13.7"
 
 [deps]
 AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"

docs/src/reference.md

@@ -23,4 +23,5 @@ Functions:
 Convex.fix!
 Convex.free!
 Convex.evaluate
+Convex.solve!
 ```

src/solution.jl

@@ -195,10 +195,30 @@ function solve!(problem::Problem{T}, optimizer; kwargs...) where {T}
     end
 end
 
+"""
+    solve!(problem::Problem{T}, optimizer::MOI.ModelLike;
+        check_vexity::Bool = true,
+        verbose::Bool = true,
+        warmstart::Bool = false,
+        silent_solver::Bool = false) where {T}
+
+Solves the problem, populating `problem.optval` with the optimal value,
+as well as the values of the variables (accessed by [`evaluate`](@ref))
+and constraint duals (accessed by `cons.dual`), where applicable.
+
+Optional keyword arguments:
+
+* `check_vexity` (default: `true`): emits a warning if the problem is not DCP
+* `verbose` (default: `true`): emits a warning if the problem was not solved optimally or `warmstart=true` but is not supported by the solver.
+* `warmstart` (default: `false`): whether the solver should start the optimization from a previous optimal value (according to the current value of the variables in the problem, which can be set by [`value!`](@ref) and accessed by [`evaluate`](@ref)).
+* `silent_solver`: whether the solver should be silent (and not emit output or logs) during the solution process.
+
+"""
 function solve!(problem::Problem{T}, optimizer::MOI.ModelLike;
-    check_vexity = true,
-    verbose = true,
-    warmstart = false) where {T}
+    check_vexity::Bool = true,
+    verbose::Bool = true,
+    warmstart::Bool = false,
+    silent_solver::Bool = false) where {T}
 
     if check_vexity
         vex = vexity(problem)
@@ -217,7 +237,11 @@ function solve!(problem::Problem{T}, optimizer::MOI.ModelLike;
     if warmstart
         warmstart_variables!(model, var_to_indices, id_to_variables, T, verbose)
     end
- 
+
+    if silent_solver
+        MOI.set(model, MOI.Silent(), true)
+    end
+
     MOI.optimize!(model)
     problem.model = model
 

src/variable.jl

@@ -98,10 +98,12 @@ sign!(x::AbstractVariable, s::Sign) = x.sign = s
 
 
 """
-    evaluate(x::AbstractVariable)
+    evaluate(x::AbstractExpr)
 
 Returns the current value of `x` if assigned; errors otherwise.
 """
+evaluate
+
 evaluate(x::AbstractVariable) = _value(x) === nothing ? error("Value of the variable is yet to be calculated") : output(_value(x))
 
 """

test/test_utilities.jl

@@ -1,5 +1,22 @@
 using Convex: AbstractExpr, ConicObj
 
+# It's not super easy to capture the output
+# I ended up using this pattern from Suppressor:
+# https://github.com/JuliaIO/Suppressor.jl/blob/b4ff08f0fe795a2ce9e592734a758c9e6d8e2bc4/src/Suppressor.jl#L124-L152
+function solve_and_return_output(problem, solver; kwargs...)
+        original_stdout = stdout
+        rd, wr = redirect_stdout()
+        out_task = @async read(rd, String)
+        try
+            solve!(problem, solver; kwargs...)
+        finally
+            Base.Libc.flush_cstdio() #  https://github.com/JuliaLang/julia/issues/31236
+            redirect_stdout(original_stdout)
+            close(wr)
+        end
+        return fetch(out_task)
+end
+
 @testset "Utilities" begin
 
     @testset "`solve!` does not return anything" begin
@@ -9,6 +26,15 @@ using Convex: AbstractExpr, ConicObj
         @test output === nothing
     end
 
+    @testset "`silent_solver` works" begin
+        x = Variable()
+        p = satisfy(x >= 0)
+        output_non_silent = solve_and_return_output(p, () -> SCS.Optimizer(eps=1e-6))
+        @test output_non_silent != ""
+        output_silent = solve_and_return_output(p, () -> SCS.Optimizer(eps=1e-6), silent_solver=true)
+        @test output_silent == ""
+    end
+
     # This might get deprecated later.
     @testset "`solve!` can take an optimizer directly" begin
         x = Variable()