Support Krylov.jl v0.10

Project.toml

@@ -15,14 +15,14 @@ SolverCore = "ff4d7338-4cf1-434d-91df-b86cb86fb843"
 SolverTools = "b5612192-2639-5dc1-abfe-fbedd65fab29"
 
 [compat]
-JSOSolvers = "0.11, 0.12, 0.13"
-Krylov = "0.8, 0.9"
-LinearOperators = "1, 2"
-NLPModels = "0.20, 0.21"
-NLPModelsModifiers = "0.6, 0.7"
+JSOSolvers = "0.14"
+Krylov = "0.10"
+LinearOperators = "2"
+NLPModels = "0.21"
+NLPModelsModifiers = "0.7"
 SolverCore = "0.3"
-SolverTools = "0.8, 0.9"
-julia = "^1.6"
+SolverTools = "0.9"
+julia = "1.10"
 
 [extras]
 ADNLPModels = "54578032-b7ea-4c30-94aa-7cbd1cce6c9a"

docs/Project.toml

@@ -12,14 +12,14 @@ SolverBenchmark = "581a75fa-a23a-52d0-a590-d6201de2218a"
 SolverCore = "ff4d7338-4cf1-434d-91df-b86cb86fb843"
 
 [compat]
-ADNLPModels = "0.7"
-CUTEst = "0.13"
+ADNLPModels = "0.8.12"
+CUTEst = "1.3.2"
 DataFrames = "1"
-Documenter = "0.27"
-GR = "0.62"
-JLD2 = "0.4"
-NLPModels = "0.20"
+Documenter = "1"
+GR = "0.73"
+JLD2 = "0.5"
+NLPModels = "0.21"
 NLPModelsIpopt = "0.10"
 Plots = "1"
-SolverBenchmark = "0.6"
+SolverBenchmark = "0.6.2"
 SolverCore = "0.3"

docs/make.jl

@@ -5,8 +5,7 @@ pages = ["Introduction" => "index.md", "Benchmark" => "benchmark.md", "Reference
 
 makedocs(
   sitename = "Percival.jl",
-  strict = true,
-  format = Documenter.HTML(prettyurls = get(ENV, "CI", nothing) == "true"),
+  format = Documenter.HTML(ansicolor = true, prettyurls = get(ENV, "CI", nothing) == "true"),
   modules = [Percival],
   pages = pages,
 )

docs/src/benchmark.md

@@ -17,7 +17,7 @@ using SolverBenchmark
 Let us select problems from CUTEst with a maximum of 100 variables or constraints. After removing problems with fixed variables, examples with a constant objective, and infeasibility residuals.
 
 ``` @example ex1
-_pnames = CUTEst.select(
+_pnames = select_sif_problems(
   max_var = 100, 
   min_con = 1, 
   max_con = 100, 

docs/src/reference.md

@@ -1,17 +1,17 @@
 # Reference
-​
+
 ## Contents
-​
+
 ```@contents
 Pages = ["reference.md"]
 ```
-​
+
 ## Index
-​
+
 ```@index
 Pages = ["reference.md"]
 ```
-​
+
 ```@autodocs
 Modules = [Percival]
-```
+```

src/method.jl

@@ -161,7 +161,7 @@ mutable struct PercivalSolver{T, V, Op, M, ST} <: AbstractOptimizationSolver
   Jv::V
   Jtv::V
   Jx::Op
-  cgls_solver::CglsSolver{T, T, V}
+  cgls_workspace::CglsWorkspace{T, T, V}
   sub_pb::AugLagModel{M, T, V}
   sub_solver::ST
   sub_stats::GenericExecutionStats{T, V, V, Any}
@@ -185,7 +185,7 @@ function PercivalSolver(
 
   Jx = jac_op!(nlp, x, Jv, Jtv)
   Op = typeof(Jx)
-  cgls_solver = CglsSolver(Jx', gx)
+  cgls_workspace = CglsWorkspace(Jx', gx)
 
   sub_pb = AugLagModel(nlp, V(undef, ncon), T(0), x, T(0), V(undef, ncon))
   model = subproblem_modifier(sub_pb)
@@ -206,7 +206,7 @@ function PercivalSolver(
     Jv,
     Jtv,
     Jx,
-    cgls_solver,
+    cgls_workspace,
     sub_pb,
     sub_solver,
     sub_stats,
@@ -345,8 +345,8 @@ function SolverCore.solve!(
   # Lagrange multiplier
   y = solver.y
   if inity
-    cgls!(solver.cgls_solver, Jx', gx, verbose = cgls_verbose)
-    y = solver.cgls_solver.x
+    cgls!(solver.cgls_workspace, Jx', gx, verbose = cgls_verbose)
+    y = solver.cgls_workspace.x
   else
     y .= nlp.meta.y0
   end