Merge remote-tracking branch 'origin/master' into vk-moi-no-nlp-block

.github/dependabot.yml

@@ -0,0 +1,7 @@
+# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
+version: 2
+updates:
+  - package-ecosystem: "github-actions"
+    directory: "/" # Location of package manifests
+    schedule:
+      interval: "weekly"

.github/workflows/CI.yml

@@ -35,11 +35,11 @@ jobs:
           - '1'
           - '1.6'
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
       - uses: julia-actions/setup-julia@v1
         with:
           version: ${{ matrix.version }}
-      - uses: actions/cache@v1
+      - uses: actions/cache@v3
         env:
           cache-name: cache-artifacts
         with:

.github/workflows/Documentation.yml

@@ -11,7 +11,7 @@ jobs:
   build:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
       - uses: julia-actions/setup-julia@latest
         with:
           version: '1'

.github/workflows/Downstream.yml

@@ -22,14 +22,14 @@ jobs:
           - {user: SciML, repo: ModelingToolkit.jl, group: All}
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
       - uses: julia-actions/setup-julia@v1
         with:
           version: ${{ matrix.julia-version }}
           arch: x64
       - uses: julia-actions/julia-buildpkg@latest
       - name: Clone Downstream
-        uses: actions/checkout@v2
+        uses: actions/checkout@v3
         with:
           repository: ${{ matrix.package.user }}/${{ matrix.package.repo }}
           path: downstream
@@ -51,6 +51,6 @@ jobs:
             exit(0)  # Exit immediately, as a success
           end
       - uses: julia-actions/julia-processcoverage@v1
-      - uses: codecov/codecov-action@v1
+      - uses: codecov/codecov-action@v3
         with:
           file: lcov.info

.github/workflows/FormatCheck.yml

@@ -21,7 +21,7 @@ jobs:
         with:
           version: ${{ matrix.julia-version }}
 
-      - uses: actions/checkout@v1
+      - uses: actions/checkout@v3
       - name: Install JuliaFormatter and format
         # This will use the latest version by default but you can set the version like so:
         #

Project.toml

@@ -1,9 +1,9 @@
 name = "Optimization"
 uuid = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
-version = "3.11.2"
+version = "3.12.1"
 
 [deps]
-ArrayInterfaceCore = "30b0a656-2188-435a-8636-2ec0e6a096e2"
+ArrayInterface = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"
 ConsoleProgressMonitor = "88cd18e8-d9cc-4ea6-8889-5259c0d15c8b"
 DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
@@ -18,8 +18,7 @@ SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 TerminalLoggers = "5d786b92-1e48-4d6f-9151-6b4477ca9bed"
 
 [compat]
-ArrayInterface = "6"
-ArrayInterfaceCore = "0.1.1"
+ArrayInterface = "6, 7"
 ConsoleProgressMonitor = "0.1"
 DocStringExtensions = "0.8, 0.9"
 LoggingExtras = "0.4, 0.5, 1"
@@ -29,6 +28,3 @@ Requires = "1.0"
 SciMLBase = "1.79.0"
 TerminalLoggers = "0.1"
 julia = "1.6"
-
-[extras]
-ArrayInterface = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"

README.md

@@ -9,6 +9,8 @@
 [![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor%27s%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
 [![SciML Code Style](https://img.shields.io/static/v1?label=code%20style&message=SciML&color=9558b2&labelColor=389826)](https://github.com/SciML/SciMLStyle)
 
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7738525.svg)](https://doi.org/10.5281/zenodo.7738525)
+
 Optimization.jl is a package with a scope that is beyond your normal global optimization
 package. Optimization.jl seeks to bring together all of the optimization packages
 it can find, local and global, into one unified Julia interface. This means, you

docs/pages.jl

@@ -1,11 +1,13 @@
 pages = ["index.md",
+    "getting_started.md",
     "Tutorials" => [
-        "tutorials/intro.md",
-        "tutorials/rosenbrock.md",
         "tutorials/minibatch.md",
         "tutorials/symbolic.md",
         "tutorials/constraints.md",
     ],
+    "Examples" => [
+        "examples/rosenbrock.md",
+    ],
     "Basics" => [
         "API/optimization_problem.md",
         "API/optimization_function.md",

docs/src/tutorials/rosenbrock.md → docs/src/examples/rosenbrock.md

@@ -1,6 +1,6 @@
 # Solving the Rosenbrock Problem in >10 Ways
 
-This tutorial is a demonstration of many different solvers to demonstrate the
+This example is a demonstration of many different solvers to demonstrate the
 flexibility of Optimization.jl. This is a gauntlet of many solvers to get a feel
 for common workflows of the package and give copy-pastable starting points.
 

docs/src/tutorials/intro.md → docs/src/getting_started.md

@@ -1,4 +1,4 @@
-# Basic usage
+# Getting Started with Optimization in Julia
 
 In this tutorial, we introduce the basics of Optimization.jl by showing
 how to easily mix local optimizers from Optim.jl and global optimizers

docs/src/optimization_packages/mathoptinterface.md

@@ -82,11 +82,15 @@ sol = solve(prob, opt)
 The following shows how to use integer linear programming within `Optimization`. We will solve the classical Knapsack Problem using `Juniper.jl`.
 
   - [`Juniper.Optimizer`](https://github.com/lanl-ansi/Juniper.jl)
+
   - Juniper requires a nonlinear optimizer to be set via the `nl_solver` option,
     which must be a MathOptInterface-based optimizer. See the
     [Juniper documentation](https://github.com/lanl-ansi/Juniper.jl) for more
     detail.
-  - Allows only for binary decisions
+  - The integer domain is infered based on the bounds of the variable:
+
+      + Setting the lower bound to zero and the upper bound to one corresponds to `MOI.ZeroOne()` or a binary decision variable
+      + Providing other or no bounds corresponds to `MOI.Integer()`
 
 ```@example MOI
 v = [1.0, 2.0, 4.0, 3.0]

lib/OptimizationMOI/Project.toml

@@ -1,7 +1,7 @@
 name = "OptimizationMOI"
 uuid = "fd9f6733-72f4-499f-8506-86b2bdd0dea1"
 authors = ["Vaibhav Dixit <[email protected]> and contributors"]
-version = "0.1.7"
+version = "0.1.8"
 
 [deps]
 MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"

lib/OptimizationMOI/test/runtests.jl

@@ -158,25 +158,43 @@ end
     sol = solve(prob, AmplNLWriter.Optimizer(Ipopt_jll.amplexe))
 end
 
-@testset "MINLP" begin
-    v = [1.0, 2.0, 4.0, 3.0]
-    w = [5.0, 4.0, 3.0, 2.0]
-    W = 4.0
-    u0 = [0.0, 0.0, 0.0, 1.0]
-
-    optfun = OptimizationFunction((u, p) -> -v'u, cons = (res, u, p) -> res .= w'u,
-                                  Optimization.AutoForwardDiff())
-
-    optprob = OptimizationProblem(optfun, u0; lb = zero.(u0), ub = one.(u0),
-                                  int = ones(Bool, length(u0)),
-                                  lcons = [-Inf;], ucons = [W;])
-
+@testset "Integer Support" begin
     nl_solver = OptimizationMOI.MOI.OptimizerWithAttributes(Ipopt.Optimizer,
                                                             "print_level" => 0)
     minlp_solver = OptimizationMOI.MOI.OptimizerWithAttributes(Juniper.Optimizer,
                                                                "nl_solver" => nl_solver)
 
-    res = solve(optprob, minlp_solver)
-    @test res.u == [0.0, 0.0, 1.0, 0.0]
-    @test res.objective == -4.0
+    @testset "Binary Domain" begin
+        v = [1.0, 2.0, 4.0, 3.0]
+        w = [5.0, 4.0, 3.0, 2.0]
+        W = 4.0
+        u0 = [0.0, 0.0, 0.0, 1.0]
+
+        optfun = OptimizationFunction((u, p) -> -v'u, cons = (res, u, p) -> res .= w'u,
+                                      Optimization.AutoForwardDiff())
+
+        optprob = OptimizationProblem(optfun, u0; lb = zero.(u0), ub = one.(u0),
+                                      int = ones(Bool, length(u0)),
+                                      lcons = [-Inf;], ucons = [W;])
+
+        res = solve(optprob, minlp_solver)
+        @test res.u == [0.0, 0.0, 1.0, 0.0]
+        @test res.objective == -4.0
+    end
+
+    @testset "Integer Domain" begin
+        x = [1.0, 2.0, 4.0, 3.0]
+        y = [5.0, 10.0, 20.0, 15.0]
+        u0 = [1.0]
+
+        optfun = OptimizationFunction((u, p) -> sum(abs2, x * u[1] .- y),
+                                      Optimization.AutoForwardDiff())
+
+        optprob = OptimizationProblem(optfun, u0; lb = one.(u0), ub = 6.0 .* u0,
+                                      int = ones(Bool, length(u0)))
+
+        res = solve(optprob, minlp_solver)
+        @test res.u ≈ [5.0]
+        @test res.objective <= 5eps()
+    end
 end

lib/OptimizationNLopt/src/OptimizationNLopt.jl

@@ -77,6 +77,31 @@ function __map_optimizer_args!(prob::OptimizationProblem, opt::NLopt.Opt;
     return nothing
 end
 
+function __nlopt_status_to_ReturnCode(status::Symbol)
+    if status in Symbol.([
+                             NLopt.SUCCESS,
+                             NLopt.STOPVAL_REACHED,
+                             NLopt.FTOL_REACHED,
+                             NLopt.XTOL_REACHED,
+                         ])
+        return ReturnCode.Success
+    elseif status == Symbol(NLopt.MAXEVAL_REACHED)
+        return ReturnCode.MaxIters
+    elseif status == Symbol(NLopt.MAXTIME_REACHED)
+        return ReturnCode.MaxTime
+    elseif status in Symbol.([
+                                 NLopt.OUT_OF_MEMORY,
+                                 NLopt.INVALID_ARGS,
+                                 NLopt.FAILURE,
+                                 NLopt.ROUNDOFF_LIMITED,
+                                 NLopt.FORCED_STOP,
+                             ])
+        return ReturnCode.Failure
+    else
+        return ReturnCode.Default
+    end
+end
+
 function SciMLBase.__solve(prob::OptimizationProblem,
                            opt::Union{NLopt.Algorithm, NLopt.Opt};
                            maxiters::Union{Number, Nothing} = nothing,
@@ -134,8 +159,9 @@ function SciMLBase.__solve(prob::OptimizationProblem,
     (minf, minx, ret) = NLopt.optimize(opt_setup, prob.u0)
     t1 = time()
 
+    retcode = __nlopt_status_to_ReturnCode(ret)
     SciMLBase.build_solution(SciMLBase.DefaultOptimizationCache(prob.f, prob.p), opt, minx,
-                             minf; original = opt_setup, retcode = ret,
+                             minf; original = opt_setup, retcode = retcode,
                              solve_time = t1 - t0)
 end
 

lib/OptimizationOptimisers/Project.toml

@@ -1,7 +1,7 @@
 name = "OptimizationOptimisers"
 uuid = "42dfb2eb-d2b4-4451-abcd-913932933ac1"
 authors = ["Vaibhav Dixit <[email protected]> and contributors"]
-version = "0.1.1"
+version = "0.1.2"
 
 [deps]
 Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"

lib/OptimizationOptimisers/src/OptimizationOptimisers.jl

@@ -4,12 +4,7 @@ using Reexport, Printf, ProgressLogging
 @reexport using Optimisers, Optimization
 using Optimization.SciMLBase
 
-const OptimisersOptimizers = Union{Descent, Adam, Momentum, Nesterov, RMSProp,
-                                   AdaGrad, AdaMax, AdaDelta, AMSGrad, NAdam, RAdam, OAdam,
-                                   AdaBelief,
-                                   WeightDecay, ClipGrad, ClipNorm, OptimiserChain}
-
-function SciMLBase.__solve(prob::OptimizationProblem, opt::OptimisersOptimizers,
+function SciMLBase.__solve(prob::OptimizationProblem, opt::AbstractRule,
                            data = Optimization.DEFAULT_DATA;
                            maxiters::Number = 0, callback = (args...) -> (false),
                            progress = false, save_best = true, kwargs...)

lib/OptimizationPolyalgorithms/Project.toml

@@ -1,7 +1,7 @@
 name = "OptimizationPolyalgorithms"
 uuid = "500b13db-7e66-49ce-bda4-eed966be6282"
 authors = ["Vaibhav Dixit <[email protected]> and contributors"]
-version = "0.1.2"
+version = "0.2.0"
 
 [deps]
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"

src/Optimization.jl

@@ -8,7 +8,7 @@ using Reexport
 @reexport using SciMLBase
 using Requires
 using Logging, ProgressLogging, ConsoleProgressMonitor, TerminalLoggers, LoggingExtras
-using ArrayInterfaceCore, Base.Iterators, SparseArrays
+using ArrayInterface, Base.Iterators, SparseArrays
 using Pkg
 
 import SciMLBase: OptimizationProblem, OptimizationFunction, AbstractADType, ObjSense,

src/function/finitediff.jl

@@ -77,7 +77,7 @@ function instantiate_function(f, x, adtype::AutoFiniteDiff, p,
 
     if f.hv === nothing
         hv = function (H, θ, v, args...)
-            res = ArrayInterfaceCore.zeromatrix(θ)
+            res = ArrayInterface.zeromatrix(θ)
             hess(res, θ, args...)
             H .= res * v
         end

src/function/forwarddiff.jl

@@ -64,7 +64,7 @@ function instantiate_function(f::OptimizationFunction{true}, x,
 
     if f.hv === nothing
         hv = function (H, θ, v, args...)
-            res = ArrayInterfaceCore.zeromatrix(θ)
+            res = ArrayInterface.zeromatrix(θ)
             hess(res, θ, args...)
             H .= res * v
         end

src/function/function.jl

@@ -43,11 +43,10 @@ function that is not defined, an error is thrown.
 For more information on the use of automatic differentiation, see the
 documentation of the `AbstractADType` types.
 """
-function instantiate_function(f, x, ::AbstractADType,
-                              p, num_cons = 0)
-    grad = f.grad === nothing ? nothing : (G, x) -> f.grad(G, x, p)
-    hess = f.hess === nothing ? nothing : (H, x) -> f.hess(H, x, p)
-    hv = f.hv === nothing ? nothing : (H, x, v) -> f.hv(H, x, v, p)
+function instantiate_function(f, x, ::AbstractADType, p, num_cons = 0)
+    grad = f.grad === nothing ? nothing : (G, x, args...) -> f.grad(G, x, p, args...)
+    hess = f.hess === nothing ? nothing : (H, x, args...) -> f.hess(H, x, p, args...)
+    hv = f.hv === nothing ? nothing : (H, x, v, args...) -> f.hv(H, x, v, p, args...)
     cons = f.cons === nothing ? nothing : (res, x) -> f.cons(res, x, p)
     cons_j = f.cons_j === nothing ? nothing : (res, x) -> f.cons_j(res, x, p)
     cons_h = f.cons_h === nothing ? nothing : (res, x) -> f.cons_h(res, x, p)
@@ -73,9 +72,9 @@ end
 
 function instantiate_function(f, cache::ReInitCache, ::AbstractADType,
                               num_cons = 0)
-    grad = f.grad === nothing ? nothing : (G, x) -> f.grad(G, x, cache.p)
-    hess = f.hess === nothing ? nothing : (H, x) -> f.hess(H, x, cache.p)
-    hv = f.hv === nothing ? nothing : (H, x, v) -> f.hv(H, x, v, cache.p)
+    grad = f.grad === nothing ? nothing : (G, x, args...) -> f.grad(G, x, cache.p, args...)
+    hess = f.hess === nothing ? nothing : (H, x, args...) -> f.hess(H, x, cache.p, args...)
+    hv = f.hv === nothing ? nothing : (H, x, v, args...) -> f.hv(H, x, v, cache.p, args...)
     cons = f.cons === nothing ? nothing : (res, x) -> f.cons(res, x, cache.p)
     cons_j = f.cons_j === nothing ? nothing : (res, x) -> f.cons_j(res, x, cache.p)
     cons_h = f.cons_h === nothing ? nothing : (res, x) -> f.cons_h(res, x, cache.p)

src/function/mtk.jl

@@ -62,7 +62,7 @@ function instantiate_function(f, x, adtype::AutoModelingToolkit, p,
 
     hv = function (H, θ, v, args...)
         res = adtype.obj_sparse ? (eltype(θ)).(f.hess_prototype) :
-              ArrayInterfaceCore.zeromatrix(θ)
+              ArrayInterface.zeromatrix(θ)
         hess(res, θ, args...)
         H .= res * v
     end

test/Project.toml

@@ -12,6 +12,7 @@ Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
+SciMLSensitivity = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
@@ -28,6 +29,7 @@ Optim = ">= 1.4.1"
 OrdinaryDiffEq = ">= 5"
 ReverseDiff = ">= 1.9.0"
 SafeTestsets = ">= 0.0.1"
+SciMLSensitivity = ">= 7.0.0"
 Tracker = ">= 0.2"
 Zygote = ">= 0.5"
 julia = "1.5"