R2NLS solver for unconstrained nonlinear least-squares (NLS) problems

Project.toml

@@ -3,25 +3,36 @@ uuid = "10dff2fc-5484-5881-a0e0-c90441020f8a"
 version = "0.14.1"
 
 [deps]
+HSL = "34c5aeac-e683-54a6-a0e9-6e0fdc586c50"
+HSL_jll = "017b0a0e-03f4-516a-9b91-836bbd1904dd"
 Krylov = "ba0b0d4f-ebba-5204-a429-3ac8c609bfb7"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LinearOperators = "5c8ed15e-5a4c-59e4-a42b-c7e8811fb125"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
 NLPModelsModifiers = "e01155f1-5c6f-4375-a9d8-616dd036575f"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
+QRMumps = "422b30a1-cc69-4d85-abe7-cc07b540c444"
 SolverCore = "ff4d7338-4cf1-434d-91df-b86cb86fb843"
 SolverParameters = "d076d87d-d1f9-4ea3-a44b-64b4cdd1e470"
 SolverTools = "b5612192-2639-5dc1-abfe-fbedd65fab29"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+SparseMatricesCOO = "fa32481b-f100-4b48-8dc8-c62f61b13870"
+TSVD = "9449cd9e-2762-5aa3-a617-5413e99d722e"
 
 [compat]
-Krylov = "0.10.0"
-LinearOperators = "2.0"
+HSL = "0.5.1"
+Krylov = "0.10.1"
+LinearOperators = "2.11.0"
 NLPModels = "0.21"
 NLPModelsModifiers = "0.7"
+QRMumps = "0.3.1"
 SolverCore = "0.3"
 SolverParameters = "0.1"
 SolverTools = "0.9"
+SparseArrays = "1.11.0"
+SparseMatricesCOO = "0.2.5"
+TSVD = "0.4.4"
 julia = "1.10"
 
 [extras]

README.md

@@ -26,14 +26,17 @@ This package provides an implementation of four classic algorithms for unconstra
     > large scale optimization. *Mathematical Programming*, 45(1), 503-528.
     > DOI: [10.1007/BF01589116](https://doi.org/10.1007/BF01589116)
 
-
+- `R2N`: An inexact second-order quadratic regularization method for unconstrained optimization (with shifted L-BFGS or shifted Hessian operator);
+
 - `R2`: a first-order quadratic regularization method for unconstrained optimization;
 
     > E. G. Birgin, J. L. Gardenghi, J. M. Martínez, S. A. Santos, Ph. L. Toint. (2017).
     > Worst-case evaluation complexity for unconstrained nonlinear optimization using
     > high-order regularized models. *Mathematical Programming*, 163(1), 359-368.
     > DOI: [10.1007/s10107-016-1065-8](https://doi.org/10.1007/s10107-016-1065-8)
 
+- `R2NLS`: an inexact second-order quadratic regularization method for nonlinear least-squares problems;
+
 - `fomo`: a first-order method with momentum for unconstrained optimization;
 
 - `tron`: a pure Julia implementation of TRON, a trust-region solver for bound-constrained optimization described in
@@ -63,7 +66,7 @@ using JSOSolvers, ADNLPModels
 
 # Rosenbrock
 nlp = ADNLPModel(x -> 100 * (x[2] - x[1]^2)^2 + (x[1] - 1)^2, [-1.2; 1.0])
-stats = lbfgs(nlp) # or trunk, tron, R2
+stats = lbfgs(nlp) # or trunk, tron, R2, R2N
 ```
 
 ## How to cite

docs/src/solvers.md

@@ -7,11 +7,12 @@
 - [`trunk`](@ref)
 - [`R2`](@ref)
 - [`fomo`](@ref)
+- [`R2N`](@ref)
 
 | Problem type          | Solvers  |
 | --------------------- | -------- |
-| Unconstrained NLP     | [`lbfgs`](@ref), [`tron`](@ref), [`trunk`](@ref), [`R2`](@ref), [`fomo`](@ref)|
-| Unconstrained NLS     | [`trunk`](@ref), [`tron`](@ref) |
+| Unconstrained NLP     | [`lbfgs`](@ref), [`tron`](@ref), [`trunk`](@ref), [`R2`](@ref), [`fomo`](@ref), ['R2N'](@ref)|
+| Unconstrained NLS     | [`trunk`](@ref), [`tron`](@ref), [`R2NLS`](@ref)|
 | Bound-constrained NLP | [`tron`](@ref) |
 | Bound-constrained NLS | [`tron`](@ref) |
 
@@ -22,5 +23,7 @@ lbfgs
 tron
 trunk
 R2
+R2NLS
 fomo
+R2N
 ```

myR2N/Project.toml

@@ -0,0 +1,2 @@
+[deps]
+HSL_jll = "017b0a0e-03f4-516a-9b91-836bbd1904dd"

my_R2N_dev/Project.toml

@@ -0,0 +1,13 @@
+[deps]
+ADNLPModels = "54578032-b7ea-4c30-94aa-7cbd1cce6c9a"
+HSL = "34c5aeac-e683-54a6-a0e9-6e0fdc586c50"
+HSL_jll = "017b0a0e-03f4-516a-9b91-836bbd1904dd"
+JSOSolvers = "10dff2fc-5484-5881-a0e0-c90441020f8a"
+Krylov = "ba0b0d4f-ebba-5204-a429-3ac8c609bfb7"
+LinearOperators = "5c8ed15e-5a4c-59e4-a42b-c7e8811fb125"
+NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
+NLPModelsModifiers = "e01155f1-5c6f-4375-a9d8-616dd036575f"
+Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
+SolverCore = "ff4d7338-4cf1-434d-91df-b86cb86fb843"
+SolverTools = "b5612192-2639-5dc1-abfe-fbedd65fab29"
+SparseMatricesCOO = "fa32481b-f100-4b48-8dc8-c62f61b13870"

my_R2N_dev/ideas_for_test.jl

@@ -0,0 +1,64 @@
+# 1. Test error for constrained problems
+@testset "Constrained Problems Error" begin
+  f(x) = (x[1] - 1.0)^2 + 100 * (x[2] - x[1]^2)^2
+  x0 = [-1.2; 1.0]
+  lvar = [-Inf; 0.1]
+  uvar = [0.5; 0.5]
+  c(x) = [x[1] + x[2] - 2; x[1]^2 + x[2]^2]
+  lcon = [0.0; -Inf]
+  ucon = [Inf; 1.0]
+  nlp_constrained = ADNLPModel(f, x0, lvar, uvar, c, lcon, ucon)
+
+  solver = R2NSolver(nlp_constrained)
+  stats = GenericExecutionStats(nlp_constrained)
+
+  @test_throws ErrorException begin
+    SolverCore.solve!(solver, nlp_constrained, stats)
+  end
+end
+
+# 2. Test error when non_mono_size < 1
+@testset "non_mono_size < 1 Error" begin
+  f(x) = (x[1] - 1)^2 + 4 * (x[2] - x[1]^2)^2
+  nlp = ADNLPModel(f, [-1.2; 1.0])
+
+  @test_throws ErrorException begin
+    R2NSolver(nlp; non_mono_size = 0)
+  end
+  @test_throws ErrorException begin
+    R2N(nlp; non_mono_size = 0)
+  end
+
+  @test_throws ErrorException begin
+    R2NSolver(nlp; non_mono_size = -1)
+  end
+  @test_throws ErrorException begin
+    R2N(nlp; non_mono_size = -1)
+  end
+end
+
+# 3. Test error when subsolver_type is ShiftedLBFGSSolver but nlp is not of type LBFGSModel
+@testset "ShiftedLBFGSSolver with wrong nlp type Error" begin
+  f(x) = (x[1] - 1)^2 + 4 * (x[2] - x[1]^2)^2
+  nlp = ADNLPModel(f, [-1.2; 1.0])
+
+  @test_throws ErrorException begin
+    R2NSolver(nlp; subsolver= :shifted_lbfgs)
+  end
+  @test_throws ErrorException begin
+    R2N(nlp; subsolver= :shifted_lbfgs)
+  end
+end
+
+# 4. Test error when subsolver_type is not a subtype of R2N_allowed_subsolvers
+@testset "Invalid subsolver type Error" begin
+  f(x) = (x[1] - 1)^2 + 4 * (x[2] - x[1]^2)^2
+  nlp = ADNLPModel(f, [-1.2; 1.0])
+
+  @test_throws ErrorException begin
+    R2NSolver(nlp; subsolver_type = CgSolver)
+  end
+  @test_throws ErrorException begin
+    R2N(nlp; subsolver_type = CgSolver)
+  end
+end