Bl/pnon

Project.toml

@@ -1,7 +1,7 @@
 name = "Penopt"
 uuid = "fb8f6f4e-b8f1-11e9-2b95-bdba3fec731e"
 authors = ["Michal Kocvara <kocvara@penopt.com>", "Benoît Legat <benoit.legat@gmail.com>", "Michael Stingl <mstingl@penopt.com>"]
-version = "0.1.0"
+version = "0.2.0"
 
 [deps]
 Libdl = "8f399da3-3557-5675-b5ff-fb832c97cbdb"

README.md

@@ -67,7 +67,7 @@ PENNON shared library or static archive, re-running `Pkg.build("Penopt")`, and
 restarting Julia, as for PENBMI. Availability is reported by
 `Penopt.has_pennon()`.
 
-`Penopt.Pennon.Optimizer` accepts nonlinear scalar objectives and constraints,
+`Penopt.NON.Optimizer` accepts nonlinear scalar objectives and constraints,
 and accepts nonlinear semidefinite constraints as an
 `MOI.VectorNonlinearFunction` in an
 `MOI.PositiveSemidefiniteConeTriangle`. Derivatives are evaluated with MOI's
@@ -80,7 +80,7 @@ session.
 
 Pick the solver explicitly: `Penopt.SDP.Optimizer` solves semidefinite programs
 with PENSDP, `Penopt.BMI.Optimizer` solves bilinear matrix inequalities and
-quadratic objectives with PENBMI, and `Penopt.Pennon.Optimizer` solves general
+quadratic objectives with PENBMI, and `Penopt.NON.Optimizer` solves general
 nonlinear semidefinite programs with PENNON.
 
 ```julia
@@ -100,6 +100,17 @@ natively by `Penopt.BMI.Optimizer`:
 model = Model(Penopt.BMI.Optimizer)
 ```
 
+For general nonlinear objectives, scalar constraints, and matrix inequalities,
+use the PENNON backend:
+
+```julia
+model = Model(Penopt.NON.Optimizer)
+```
+
+Enable the PENNON library as described in [PENNON](#pennon) above. MOI bridges
+automatically convert affine and quadratic functions to nonlinear functions,
+so JuMP expressions do not need explicit nonlinear conversion.
+
 ## Options
 
 See the [Penbmi Documentation](http://www.penopt.com/doc/penbmi2_1.pdf)

src/Pennon.jl

@@ -503,17 +503,17 @@ function MOI.get(model::PennonOptimizer, attr::MOI.PrimalStatus)
     return model.info == 0 ? MOI.FEASIBLE_POINT : MOI.UNKNOWN_RESULT_STATUS
 end
 
-module Pennon
+module NON
 
 import ..Penopt
 
 """
-    Penopt.Pennon.Optimizer()
+    Penopt.NON.Optimizer()
 
 Optimizer for nonlinear semidefinite programs. Matrix constraints are accepted
 as `MOI.VectorNonlinearFunction`-in-`MOI.PositiveSemidefiniteConeTriangle` and
 their derivatives are supplied to PENNON by MOI's sparse reverse-mode AD.
 """
 const Optimizer = Penopt.PennonOptimizer
 
-end # module Pennon
+end # module NON

src/Penopt.jl

@@ -33,7 +33,7 @@ has_penbmi() = !isempty(libpenbmi)
 """
     has_pennon()
 
-Return whether the PENNON library used by [`Penopt.Pennon.Optimizer`](@ref) was
+Return whether the PENNON library used by [`Penopt.NON.Optimizer`](@ref) was
 found by `Pkg.build("Penopt")`.
 """
 has_pennon() = !isempty(libpennon)

test/pennon.jl

@@ -12,13 +12,13 @@ import MathOptInterface as MOI
 import Penopt
 
 function _model()
-    model = Model(Penopt.Pennon.Optimizer)
+    model = Model(Penopt.NON.Optimizer)
     set_silent(model)
     return model
 end
 
 function test_interface()
-    model = Penopt.Pennon.Optimizer()
+    model = Penopt.NON.Optimizer()
     @test MOI.get(model, MOI.SolverName()) == "Pennon"
     @test MOI.supports_incremental_interface(model)
     @test MOI.get(model, MOI.Bridges.ListOfNonstandardBridges{Float64}()) ==
@@ -68,7 +68,7 @@ function test_interface()
 end
 
 function test_scalar_constraint_dispatch()
-    model = Penopt.Pennon.Optimizer()
+    model = Penopt.NON.Optimizer()
     x = MOI.add_variable(model)
     @test MOI.get(model, MOI.NumberOfVariables()) == 1
     f = MOI.ScalarNonlinearFunction(:sin, Any[x])