JuMP Interface documentation update (#62)

* jump interface documentation

* MadNLPGPU added

* MadNLP bump version

---------

Co-authored-by: Sungho Shin <[email protected]>

Author: sshin23

Project.toml

@@ -43,7 +43,7 @@ oneAPI = "1"
 MathOptInterface = "1.19"
 Ipopt = "1.6"
 NLPModelsIpopt = "0.10"
-MadNLP = "0.7"
+MadNLP = "0.8"
 
 [extras]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"

README.md

@@ -1,4 +1,4 @@
-![Logo](full-logo.svg) 
+![Logo](full-logo.svg)
 
 *An [algebraic modeling](https://en.wikipedia.org/wiki/Algebraic_modeling_language) and [automatic differentiation](https://en.wikipedia.org/wiki/Automatic_differentiation) tool in [Julia Language](https://julialang.org/), specialized for [SIMD](https://en.wikipedia.org/wiki/Single_instruction,_multiple_data) abstraction of [nonlinear programs](https://en.wikipedia.org/wiki/Nonlinear_programming).*
 

docs/Project.toml

@@ -7,6 +7,7 @@ JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
 KernelAbstractions = "63c18a36-062a-441e-b654-da1e3ab1ce7c"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
 MadNLP = "2621e9c9-9eb4-46b1-8089-e8c72242dfb6"
+MadNLPGPU = "d72a61cc-809d-412f-99be-fd81f4b8a598"
 MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
 NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
 NLPModelsIpopt = "f4238b75-b362-5c4c-b852-0801c9a21d71"

docs/make.jl

@@ -10,14 +10,14 @@ if !(@isdefined _PAGES)
         "Mathematical Abstraction" => "simd.md",
         "Tutorial" => [
             "guide.md",
-            "jump.md",
             "performance.md",
             "gpu.md",
             "develop.md",
             "quad.md",
             "distillation.md",
             "opf.md",
         ],
+        "JuMP Interface (experimental)" => "jump.md",
         "API Manual" => "core.md",
         "References" => "ref.md",
     ]

docs/src/jump.jl

@@ -1,8 +1,9 @@
-# # JuMP Interface
+# # JuMP Interface (Experimental)
 
+# ## JuMP to an ExaModel
 # We have an experimental interface to JuMP model. A JuMP model can be directly converted to a `ExaModel`. It is as simple as this:
 
-using ExaModels, JuMP
+using ExaModels, JuMP, CUDA
 
 N = 10
 jm = Model()
@@ -16,11 +17,23 @@ jm = Model()
 )
 @objective(jm, Min, sum(100(x[i-1]^2 - x[i])^2 + (x[i-1] - 1)^2 for i = 2:N))
 
-em = ExaModel(jm)
+em = ExaModel(jm; backend = CUDABackend())
 
 # Here, note that only scalar objective/constraints created via `@constraint` and `@objective` API are supported. Older syntax like `@NLconstraint` and `@NLobjective` are not supported.
-# We can solve the model using any of the solvers supported by ExaModels. For example, we can use Ipopt:
+# We can solve the model using any of the solvers supported by ExaModels. For example, we can use MadNLP:
 
-using NLPModelsIpopt
+using MadNLP, MadNLPGPU
 
-result = ipopt(em)
+result = madnlp(em)
+
+
+# ## JuMP Optimizer
+# Alternatively, one can use the `Optimizer` interface provided by `ExaModels`. This feature can be used as follows.
+
+using ExaModels, JuMP, CUDA
+using MadNLP, MadNLPGPU
+
+set_optimizer(jm, () -> ExaModels.MadNLPOptimizer(CUDABackend()))
+optimize!(jm)
+
+# Again, only scalar objective/constraints created via `@constraint` and `@objective` API are supported. Older syntax like `@NLconstraint` and `@NLobjective` are not supported.