update documentation

exanauts · Mar 10, 2024 · 38f5c20 · 38f5c20
1 parent 8588897
commit 38f5c20
Show file tree

Hide file tree

Showing 5 changed files with 17 additions and 30 deletions.
diff --git a/.ci/setup.jl b/.ci/setup.jl
@@ -1,10 +1,9 @@
 
 using Pkg
-Pkg.instantiate()
-
-using CUDA
-
 argos_path = joinpath(@__DIR__, "..")
 Pkg.develop(path=argos_path)
 
+Pkg.instantiate()
+
+using CUDA
 CUDA.set_runtime_version!(v"11.8")
diff --git a/docs/src/lib/kkt.md b/docs/src/lib/kkt.md
@@ -4,10 +4,9 @@ CurrentModule = Argos
 
 # KKT systems
 
-Argos implements two KKT systems [`MadNLP.AbstractKKTSystem`](https://madnlp.github.io/MadNLP.jl/dev/lib/kkt/#MadNLP.AbstractKKTSystem)
+Argos implements a MadNLP's KKT system [`MadNLP.AbstractKKTSystem`](https://madnlp.github.io/MadNLP.jl/dev/lib/kkt/#MadNLP.AbstractKKTSystem)
 whose operations can be deported on NVIDIA GPU.
 
 ```@docs
 BieglerKKTSystem
-MixedAuglagKKTSystem
 ```
diff --git a/docs/src/optim/biegler.md b/docs/src/optim/biegler.md
@@ -52,13 +52,17 @@ KKT = Argos.BieglerKKTSystem{Float64, Vector{Int}, Vector{Float64}, Matrix{Float
 and we instantiate MadNLP with:
 ```@example bieglermadnlp
 using MadNLP
-# This syntax is a bit too involved and should be improved in the future.
-madnlp_options = Dict{Symbol, Any}()
-madnlp_options[:linear_solver] = MadNLP.LapackCPUSolver
-opt_ipm, opt_linear, logger = MadNLP.load_options(; madnlp_options...)
 
-KKT = Argos.BieglerKKTSystem{Float64, Vector{Int}, Vector{Float64}, Matrix{Float64}}
-solver = MadNLP.MadNLPSolver{Float64, KKT}(model, opt_ipm, opt_linear; logger=logger)
+T = Float64
+VI = Vector{Int}
+VT = Vector{T}
+MT = Matrix{T}
+solver = MadNLP.MadNLPSolver(
+    model;
+    kkt_system=Argos.BieglerKKTSystem{T, VI, VT, MT},
+    linear_solver=LapackCPUSolver,
+    callback=MadNLP.SparseCallback,
+)
 ```
 Note that we are again using Lapack as linear solver: indeed the resulting Biegler's KKT
 system is dense (we use the same condensification procedure as in the

diff --git a/docs/src/optim/reducedspace.md b/docs/src/optim/reducedspace.md
@@ -56,7 +56,7 @@ a dense linear solver (as Lapack):
 ```@example reducedmadnlp
 solver = MadNLP.MadNLPSolver(
     model;
-    kkt_system=MadNLP.DENSE_KKT_SYSTEM,
+    kkt_system=MadNLP.DenseKKTSystem,
     linear_solver=LapackCPUSolver,
 )
 MadNLP.get_kkt(solver.kkt)
@@ -72,7 +72,7 @@ only proportional to the number of variables (here, `5`):
 ```@example reducedmadnlp
 solver = MadNLP.MadNLPSolver(
     model;
-    kkt_system=MadNLP.DENSE_CONDENSED_KKT_SYSTEM,
+    kkt_system=MadNLP.DenseCondensedKKTSystem,
     linear_solver=LapackCPUSolver,
 )
 MadNLP.get_kkt(solver.kkt)
@@ -105,7 +105,7 @@ We recommend changing the default tolerance to be above the tolerance
     using MadNLPGPU
     solver = MadNLP.MadNLPSolver(
         model;
-        kkt_system=MadNLP.DENSE_CONDENSED_KKT_SYSTEM,
+        kkt_system=MadNLP.DenseCondensedKKTSystem,
         linear_solver=LapackGPUSolver,
         tol=1e=5,
     )

diff --git a/docs/src/quickstart/cpu.md b/docs/src/quickstart/cpu.md
@@ -36,21 +36,6 @@ datafile = joinpath(INSTANCES_DIR, "case118.m")
 
 ```
 
-## Full-space method
-
-!!! tip
-    At each iteration of the algorithm,
-    `FullSpace` solves the KKT system with a sparse linear solver.
-    By default, MadNLP is using Umfpack, but we recommend installing
-    [MadNLPHSL](https://madnlp.github.io/MadNLP.jl/dev/installation/#HSL-linear-solver)
-    and uses ma27 (`linear_solver=Ma27Solver`) or ma57 (`linear_solver=Ma57Solver`).
-
-
-```@repl quickstart_cpu
-Argos.run_opf(datafile, Argos.FullSpace());
-
-```
-
 ## Biegler's method (linearize-then-reduce)
 
 !!! tip