Merge branch 'master' into master

Author: Vaibhavdixit02

.github/workflows/CI.yml

@@ -36,6 +36,7 @@ jobs:
           - OptimizationQuadDIRECT
           - OptimizationSpeedMapping
           - OptimizationPolyalgorithms
+          - OptimizationNLPModels
         version:
           - '1'
     steps:

.github/workflows/Downgrade.yml

@@ -12,7 +12,6 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 LoggingExtras = "e6f89c97-d47a-5376-807f-9c37f3926c36"
 OptimizationBase = "bca83a33-5cc9-4baa-983d-23429ab6bcbb"
-Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 ProgressLogging = "33c8b6b6-d38a-422a-b730-caa89a2f386c"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
@@ -30,7 +29,6 @@ LinearAlgebra = "1.10"
 Logging = "1.10"
 LoggingExtras = "0.4, 1"
 OptimizationBase = "1.3.3"
-Pkg = "1"
 Printf = "1.10"
 ProgressLogging = "0.1"
 Reexport = "1.2"

Project.toml

@@ -12,6 +12,8 @@ Juniper = "2ddba703-00a4-53a7-87a5-e8b9971dde84"
 Manifolds = "1cead3c2-87b3-11e9-0ccd-23c62b72b94e"
 Manopt = "0fc0a36d-df90-57f3-8f93-d78a9fc72bb5"
 ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
+NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
+NLPModelsTest = "7998695d-6960-4d3a-85c4-e1bceb8cd856"
 NLopt = "76087f3c-5699-56af-9a33-bf431cd00edd"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 OptimizationBBO = "3e6eede4-6085-4f62-9a71-46d9bc1eb92b"
@@ -24,6 +26,7 @@ OptimizationMOI = "fd9f6733-72f4-499f-8506-86b2bdd0dea1"
 OptimizationManopt = "e57b7fff-7ee7-4550-b4f0-90e9476e9fb6"
 OptimizationMetaheuristics = "3aafef2f-86ae-4776-b337-85a36adf0b55"
 OptimizationMultistartOptimization = "e4316d97-8bbb-4fd3-a7d8-3851d2a72823"
+OptimizationNLPModels = "064b21be-54cf-11ef-1646-cdfee32b588f"
 OptimizationNLopt = "4e6fcdb7-1186-4e1f-a706-475e75c168bb"
 OptimizationNOMAD = "2cab0595-8222-4775-b714-9828e6a9e01b"
 OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
@@ -35,6 +38,8 @@ OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 SciMLSensitivity = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
+SymbolicAnalysis = "4297ee4d-0239-47d8-ba5d-195ecdf594fe"
+Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
@@ -51,6 +56,8 @@ Juniper = "0.9"
 Manifolds = "0.9"
 Manopt = "0.4"
 ModelingToolkit = "9"
+NLPModels = "0.21"
+NLPModelsTest = "0.10"
 NLopt = "0.6, 1"
 Optimization = "3"
 OptimizationBBO = "0.1, 0.2, 0.3"
@@ -63,6 +70,7 @@ OptimizationMOI = "0.1, 0.2, 0.3, 0.4"
 OptimizationManopt = "0.0.2, 0.0.3"
 OptimizationMetaheuristics = "0.1, 0.2"
 OptimizationMultistartOptimization = "0.1, 0.2"
+OptimizationNLPModels = "0.0.1"
 OptimizationNLopt = "0.1, 0.2"
 OptimizationNOMAD = "0.1, 0.2"
 OptimizationOptimJL = "0.1, 0.2, 0.3"
@@ -74,5 +82,7 @@ OrdinaryDiffEq = "6"
 ReverseDiff = ">= 1.9.0"
 SciMLBase = "2.30.0"
 SciMLSensitivity = "7"
+SymbolicAnalysis = "0.3"
+Symbolics = "6"
 Tracker = ">= 0.2"
 Zygote = ">= 0.5"

docs/Project.toml

@@ -36,6 +36,7 @@ pages = ["index.md",
         "PRIMA.jl" => "optimization_packages/prima.md",
         "Polyalgorithms.jl" => "optimization_packages/polyopt.md",
         "QuadDIRECT.jl" => "optimization_packages/quaddirect.md",
-        "SpeedMapping.jl" => "optimization_packages/speedmapping.md"
+        "SpeedMapping.jl" => "optimization_packages/speedmapping.md",
+        "NLPModels.jl" => "optimization_packages/nlpmodels.md"
     ]
 ]

docs/pages.jl

@@ -1,41 +1,39 @@
-# Getting Started with Optimization in Julia
+# Getting Started with Optimization.jl
 
 In this tutorial, we introduce the basics of Optimization.jl by showing
-how to easily mix local optimizers from Optim.jl and global optimizers
-from BlackBoxOptim.jl on the Rosenbrock equation. The simplest copy-pasteable
-code to get started is the following:
+how to easily mix local optimizers and global optimizers on the Rosenbrock equation.
+The simplest copy-pasteable code using a quasi-Newton method (LBFGS) to solve the Rosenbrock problem is the following:
 
 ```@example intro
 # Import the package and define the problem to optimize
-using Optimization
+using Optimization, Zygote
 rosenbrock(u, p) = (p[1] - u[1])^2 + p[2] * (u[2] - u[1]^2)^2
 u0 = zeros(2)
 p = [1.0, 100.0]
 
-prob = OptimizationProblem(rosenbrock, u0, p)
-
-# Import a solver package and solve the optimization problem
-using OptimizationOptimJL
-sol = solve(prob, NelderMead())
+optf = OptimizationFunction(rosenbrock, AutoZygote())
+prob = OptimizationProblem(optf, u0, p)
 
-# Import a different solver package and solve the optimization problem a different way
-using OptimizationBBO
-prob = OptimizationProblem(rosenbrock, u0, p, lb = [-1.0, -1.0], ub = [1.0, 1.0])
-sol = solve(prob, BBO_adaptive_de_rand_1_bin_radiuslimited())
+sol = solve(prob, Optimization.LBFGS())
 ```
 
-Notice that Optimization.jl is the core glue package that holds all the common
-pieces, but to solve the equations, we need to use a solver package. Here, OptimizationOptimJL
-is for [Optim.jl](https://github.com/JuliaNLSolvers/Optim.jl) and OptimizationBBO is for
-[BlackBoxOptim.jl](https://github.com/robertfeldt/BlackBoxOptim.jl).
+## Import a different solver package and solve the problem
+
+OptimizationOptimJL is a wrapper for [Optim.jl](https://github.com/JuliaNLSolvers/Optim.jl) and OptimizationBBO is a wrapper for [BlackBoxOptim.jl](https://github.com/robertfeldt/BlackBoxOptim.jl).
 
-The output of the first optimization task (with the `NelderMead()` algorithm)
-is given below:
+First let's use the NelderMead a derivative free solver from Optim.jl:
 
 ```@example intro
-optf = OptimizationFunction(rosenbrock, Optimization.AutoForwardDiff())
-prob = OptimizationProblem(optf, u0, p, lb = [-1.0, -1.0], ub = [1.0, 1.0])
-sol = solve(prob, NelderMead())
+using OptimizationOptimJL
+sol = solve(prob, Optim.NelderMead())
+```
+
+BlackBoxOptim.jl offers derivative-free global optimization solvers that requrie the bounds to be set via `lb` and `ub` in the `OptimizationProblem`. Let's use the BBO_adaptive_de_rand_1_bin_radiuslimited() solver:
+
+```@example intro
+using OptimizationBBO
+prob = OptimizationProblem(rosenbrock, u0, p, lb = [-1.0, -1.0], ub = [1.0, 1.0])
+sol = solve(prob, BBO_adaptive_de_rand_1_bin_radiuslimited())
 ```
 
 The solution from the original solver can always be obtained via `original`:

docs/src/getting_started.md

@@ -29,8 +29,9 @@ Pkg.add("Optimization")
 
 The packages relevant to the core functionality of Optimization.jl will be imported
 accordingly and, in most cases, you do not have to worry about the manual
-installation of dependencies. However, you will need to add the specific optimizer
-packages.
+installation of dependencies. [Optimization.jl](@ref) natively offers a LBFGS solver
+but for more solver choices (discussed below in Optimization Packages), you will need
+to add the specific wrapper packages.
 
 ## Contributing
 
@@ -48,32 +49,128 @@ packages.
       + On the [Julia Discourse forums](https://discourse.julialang.org)
       + See also [SciML Community page](https://sciml.ai/community/)
 
-## Overview of the Optimizers
-
-| Package                 | Local Gradient-Based | Local Hessian-Based | Local Derivative-Free | Box Constraints | Local Constrained | Global Unconstrained | Global Constrained   |
-|:----------------------- |:--------------------:|:-------------------:|:---------------------:|:---------------:|:-----------------:|:--------------------:|:--------------------:|
-| BlackBoxOptim           | ❌                    | ❌                   | ❌                     | ✅               | ❌                 | ✅                    | ❌                  ✅ |
-| CMAEvolutionaryStrategy | ❌                    | ❌                   | ❌                     | ✅               | ❌                 | ✅                    | ❌                    |
-| Evolutionary            | ❌                    | ❌                   | ❌                     | ✅               | ❌                 | ✅                    | 🟡                    |
-| Flux                    | ✅                    | ❌                   | ❌                     | ❌               | ❌                 | ❌                    | ❌                    |
-| GCMAES                  | ❌                    | ❌                   | ❌                     | ✅               | ❌                 | ✅                    | ❌                    |
-| MathOptInterface        | ✅                    | ✅                   | ✅                     | ✅               | ✅                 | ✅                    | 🟡                    |
-| MultistartOptimization  | ❌                    | ❌                   | ❌                     | ✅               | ❌                 | ✅                    | ❌                    |
-| Metaheuristics          | ❌                    | ❌                   | ❌                     | ✅               | ❌                 | ✅                    | 🟡                    |
-| NOMAD                   | ❌                    | ❌                   | ❌                     | ✅               | ❌                 | ✅                    | 🟡                    |
-| NLopt                   | ✅                    | ❌                   | ✅                     | ✅               | 🟡                 | ✅                    | 🟡                    |
-| Optim                   | ✅                    | ✅                   | ✅                     | ✅               | ✅                 | ✅                    | ✅                    |
-| PRIMA                   | ❌                    | ❌                   | ✅                     | ✅               | ✅                 | ❌                    | ❌                    |
-| QuadDIRECT              | ❌                    | ❌                   | ❌                     | ✅               | ❌                 | ✅                    | ❌                    |
-
-✅ = supported
-
-🟡 = supported in downstream library but not yet implemented in `Optimization`; PR to add this functionality are welcome
-
-❌ = not supported
+## Overview of the solver packages in alphabetical order
 
+<details>
+  <summary><strong>BlackBoxOptim</strong></summary>
+  - **Global Methods**
+    - Zeroth order
+    - Unconstrained
+    - Box Constraints
+</details>
+<details>
+  <summary><strong>CMAEvolutionaryStrategy</strong></summary>
+  - **Global Methods**
+    - Zeroth order
+    - Unconstrained
+    - Box Constraints
+</details>
+<details>
+  <summary><strong>Evolutionary</strong></summary>
+  - **Global Methods**
+    - Zeroth order
+    - Unconstrained
+    - Box Constraints
+    - Non-linear Constraints
+</details>
+<details>
+  <summary><strong>GCMAES</strong></summary>
+  - **Global Methods**
+    - First order
+    - Box Constraints
+    - Unconstrained
+</details>
+<details>
+  <summary><strong>Manopt</strong></summary>
+  - **Local Methods**
+    - First order
+    - Second order
+    - Zeroth order
+    - Box Constraints
+    - Constrained 🟡
+  - **Global Methods**
+    - Zeroth order
+    - Unconstrained
+</details>
+<details>
+  <summary><strong>MathOptInterface</strong></summary>
+  - **Local Methods**
+    - First order
+    - Second order
+    - Box Constraints
+    - Constrained
+  - **Global Methods**
+    - First order
+    - Second order
+    - Constrained
+</details>
+<details>
+  <summary><strong>MultistartOptimization</strong></summary>
+  - **Global Methods**
+    - Zeroth order
+    - First order
+    - Second order
+    - Box Constraints
+</details>
+<details>
+  <summary><strong>Metaheuristics</strong></summary>
+  - **Global Methods**
+    - Zeroth order
+    - Unconstrained
+    - Box Constraints
+</details>
+<details>
+  <summary><strong>NOMAD</strong></summary>
+  - **Global Methods**
+    - Zeroth order
+    - Unconstrained
+    - Box Constraints
+    - Constrained 🟡
+</details>
+<details>
+  <summary><strong>NLopt</strong></summary>
+  - **Local Methods**
+    - First order
+    - Zeroth order
+    - Second order 🟡
+    - Box Constraints
+    - Local Constrained 🟡
+  - **Global Methods**
+    - Zeroth order
+    - First order
+    - Unconstrained
+    - Constrained 🟡
+</details>
+<details>
+  <summary><strong>Optim</strong></summary>
+  - **Local Methods**
+    - Zeroth order
+    - First order
+    - Second order
+    - Box Constraints
+    - Constrained
+  - **Global Methods**
+    - Zeroth order
+    - Unconstrained
+    - Box Constraints
+</details>
+<details>
+  <summary><strong>PRIMA</strong></summary>
+  - **Local Methods**
+    - Derivative-Free: ✅
+  - **Constraints**
+    - Box Constraints: ✅
+    - Local Constrained: ✅
+</details>
+<details>
+  <summary><strong>QuadDIRECT</strong></summary>
+  - **Constraints**
+    - Box Constraints: ✅
+  - **Global Methods**
+    - Unconstrained: ✅
+</details>
+🟡 = supported in downstream library but not yet implemented in `Optimization.jl`; PR to add this functionality are welcome
 ## Citation
-
 ```
 @software{vaibhav_kumar_dixit_2023_7738525,
 	author = {Vaibhav Kumar Dixit and Christopher Rackauckas},