Support Krylov.jl v0.10

Project.toml

@@ -16,12 +16,12 @@ Stopping = "c4fe5a9e-e7fb-5c3d-89d5-7f405ab2214f"
 StoppingInterface = "53949629-e8ff-437b-b6e7-14da1a85bf25"
 
 [compat]
-Krylov = "^0.8.2, 0.9"
-LinearOperators = "1, 2"
-NLPModels = "0.18, 0.19, 0.20, 0.21"
-SolverCore = "0.2, 0.3"
-SolverTools = "^0.8.8, 0.9"
-SparseMatricesCOO = "0.1, 0.2"
-Stopping = "0.6.4"
-StoppingInterface = "0.3, 0.4, 0.5"
-julia = "^1.10"
+Krylov = "0.10"
+LinearOperators = "2"
+NLPModels = "0.21.5"
+SolverCore = "0.3"
+SolverTools = "0.9"
+SparseMatricesCOO = "0.2.4"
+Stopping = "0.6.5"
+StoppingInterface = "0.5.4"
+julia = "1.10"

docs/Project.toml

@@ -15,17 +15,17 @@ SolverBenchmark = "581a75fa-a23a-52d0-a590-d6201de2218a"
 SolverCore = "ff4d7338-4cf1-434d-91df-b86cb86fb843"
 
 [compat]
-ADNLPModels = "0.7"
-CUTEst = "0.13"
+ADNLPModels = "0.8"
+CUTEst = "1.3.1"
 DataFrames = "1"
 Documenter = "0.27"
 GR = "0.72"
 JLD2 = "0.4"
-JSOSolvers = "0.11"
-Krylov = "0.9"
-NLPModels = "0.20"
+JSOSolvers = "0.14"
+Krylov = "0.10"
+NLPModels = "0.21"
 NLPModelsIpopt = "0.10"
-OptimizationProblems = "0.7"
+OptimizationProblems = "0.9"
 Plots = "1"
-SolverBenchmark = "0.5"
+SolverBenchmark = "0.6"
 SolverCore = "0.3"

docs/src/3-doityourself.md

@@ -33,7 +33,7 @@ mutable struct PDataST{S,T} <: AdaptiveRegularization.TPData{T}
     cgrtol                    # Relative tolerance for `cg_lanczos`
 
     OK::Bool                  # Mandatory: preprocess success
-    solver::CgSolver          # Memory pre-allocation for `cg_lanczos`
+    solver::CgWorkspace       # Memory pre-allocation for `cg_lanczos`
 end
 ```
 

src/AdaptiveRegularization.jl

@@ -7,7 +7,8 @@ using Krylov, LinearOperators, NLPModels, SparseMatricesCOO, SolverCore, SolverT
 # Stopping
 using Stopping, StoppingInterface
 
-using Krylov: Krylov, CgLanczosShiftSolver, CgSolver, CglsSolver, LsqrSolver, cg!, cg_lanczos_shift!
+using Krylov: Krylov, cg!, cg_lanczos_shift!
+using Krylov: CgLanczosShiftWorkspace, CgWorkspace, CglsWorkspace, LsqrWorkspace, krylov_solve!
 using LinearAlgebra: LinearAlgebra, Symmetric, mul!, norm
 using LinearOperators: LinearOperators, LinearOperator
 using NLPModels:

src/SolveModel/SolveModelNLSST_TR.jl

@@ -19,7 +19,7 @@ function solve_model!(
   cgrtol = PData.cgrtol(ζ, ξ, maxtol, mintol, norm_∇f)
 
   solver = PData.solver
-  Krylov.solve!(
+  krylov_solve!(
     solver,
     Jx,
     Fx,

src/utils/pdata_struct.jl

@@ -68,7 +68,7 @@ mutable struct PDataKARC{S, T, Fatol, Frtol} <: PDataIter{T}
   norm_dirs::Array{T, 1}     # norms of xShifts
   OK::Bool                  # preprocess success
 
-  solver::CgLanczosShiftSolver{T, T, S}
+  solver::CgLanczosShiftWorkspace{T, T, S}
 end
 
 function PDataKARC(
@@ -95,7 +95,7 @@ function PDataKARC(
   end
   norm_dirs = S(undef, nshifts)
   OK = true
-  solver = CgLanczosShiftSolver(n, n, nshifts, S)
+  solver = CgLanczosShiftWorkspace(n, n, nshifts, S)
   return PDataKARC(
     d,
     λ,
@@ -139,7 +139,7 @@ mutable struct PDataTRK{S, T, Fatol, Frtol} <: PDataIter{T}
   norm_dirs::Array{T, 1}     # norms of xShifts
   OK::Bool                  # preprocess success
 
-  solver::CgLanczosShiftSolver{T, T, S}
+  solver::CgLanczosShiftWorkspace{T, T, S}
 end
 
 function PDataTRK(
@@ -166,7 +166,7 @@ function PDataTRK(
   end
   norm_dirs = S(undef, nshifts)
   OK = true
-  solver = CgLanczosShiftSolver(n, n, nshifts, S)
+  solver = CgLanczosShiftWorkspace(n, n, nshifts, S)
   return PDataTRK(
     d,
     λ,
@@ -202,7 +202,7 @@ mutable struct PDataST{S, T, Fatol, Frtol} <: PDataIter{T}
   cgrtol::Frtol
 
   OK::Bool    # preprocess success
-  solver::CgSolver{T, T, S}
+  solver::CgWorkspace{T, T, S}
 end
 
 function PDataST(
@@ -220,7 +220,7 @@ function PDataST(
   d = S(undef, n)
   λ = zero(T)
   OK = true
-  solver = CgSolver(n, n, S)
+  solver = CgWorkspace(n, n, S)
   return PDataST(d, λ, ζ, ξ, maxtol, mintol, cgatol, cgrtol, OK, solver)
 end
 
@@ -239,7 +239,7 @@ mutable struct PDataNLSST{S, T, Fatol, Frtol} <: PDataIterLS{T}
   cgrtol::Frtol
 
   OK::Bool    # preprocess success
-  solver::Union{CglsSolver{T, T, S}, LsqrSolver{T, T, S}}
+  solver::Union{CglsWorkspace{T, T, S}, LsqrWorkspace{T, T, S}}
 end
 
 function PDataNLSST(
@@ -260,9 +260,9 @@ function PDataNLSST(
   λ = zero(T)
   OK = true
   solver = if solver_method == :cgls
-    CglsSolver(m, n, S)
+    CglsWorkspace(m, n, S)
   else
-    LsqrSolver(m, n, S)
+    LsqrWorkspace(m, n, S)
   end
   return PDataNLSST(d, λ, ζ, ξ, maxtol, mintol, cgatol, cgrtol, OK, solver)
 end

test/Project.toml

@@ -17,7 +17,7 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]
 ADNLPModels = "0.8"
-Krylov = "0.9"
+Krylov = "0.10"
 LinearOperators = "2"
 NLPModels = "0.21"
 NLPModelsTest = "0.10"
@@ -26,6 +26,6 @@ SolverCore = "0.3"
 SolverTest = "0.3"
 SolverTools = "0.9"
 SparseMatricesCOO = "0.2"
-Stopping = "0.6.4"
+Stopping = "0.6"
 StoppingInterface = "0.5"
-julia = "^1.6"
+julia = "^1.10"