move Ipopt to extensions

Author: jbytecode

Project.toml

@@ -5,14 +5,18 @@ authors = ["Mehmet Hakan Satman <mhsatman@gmail.com>"]
 
 [deps]
 HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"
-Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
+[weakdeps]
+Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"
+
+[extensions]
+OperationsResearchModelsIpoptExt = "Ipopt"
+
 [compat]
 HiGHS = "1.4"
-Ipopt = "1.7"
 JuMP = "1"
 LinearAlgebra = "1"
 Statistics = "1"

ext/OperationsResearchModelsIpoptExt.jl

@@ -0,0 +1,74 @@
+module OperationsResearchModelsIpoptExt
+
+using OperationsResearchModels
+using Ipopt
+using JuMP
+using LinearAlgebra
+using Statistics
+
+
+import OperationsResearchModels: solve
+import OperationsResearchModels.Portfolio: PortfolioProblem, PortfolioResult
+
+"""
+    solve(problem)
+
+# Description
+
+Solves a portfolio optimization problem given by an object of in type `PortfolioProblem`.
+The optimization problem is formulated as a quadratic programming problem where the objective
+is to minimize the portfolio variance (risk) subject to constraints on the expected return and the weights.
+
+Mathematically, the problem can be stated as:
+
+Minimize: w' * Covmat * w
+Subject to:
+- sum(w) == 1 (the weights must sum to 1)
+- sum(w[i] * means[i] for i in 1:m) >= thresholdreturn
+- 0 <= w[i] <= 1 for all i (weights must be between 0 and 1)
+
+Where:
+- w is the vector of asset weights
+- Covmat is the covariance matrix of asset returns
+- means is the vector of expected returns for each asset
+- thresholdreturn is the minimum expected return required for the portfolio
+
+# Note
+
+Ipopt is used to solve the quadratic programming problem. Make sure to have 
+Ipopt installed and properly configured in your Julia environment to use this function.
+
+
+# Arguments
+
+- `problem::PortfolioProblem`: The problem in type of PortfolioProblem
+
+# Returns
+
+- `PortfolioResult`: The result of the portfolio optimization problem, containing the optimal weights, expected return, and the JuMP model used to solve the problem.
+
+"""
+function solve(p::PortfolioProblem)::PortfolioResult
+    model = Model(Ipopt.Optimizer)
+
+    MOI.set(model, MOI.Silent(), true)
+
+    means = mean(p.returns, dims = 1)
+    covmat = cov(p.returns)
+
+    _, m = size(p.returns)
+
+    @variable(model, 0 <= w[1:m] <= 1)
+    @constraint(model, sum(w) == 1)
+    @constraint(model, sum(w[i] * means[i] for i in 1:m) >= p.thresholdreturn)
+    @objective(model, Min, w' * covmat * w)
+
+    optimize!(model)
+
+    weights = value.(w)
+    expectedreturn = sum(weights[i] * means[i] for i in 1:m)
+
+    return PortfolioResult(weights, expectedreturn, model)
+end
+
+end

src/portfolio.jl

@@ -1,6 +1,6 @@
 module Portfolio
 
-using JuMP, Ipopt, Statistics, LinearAlgebra
+using JuMP, Statistics, LinearAlgebra
 
 import ..OperationsResearchModels: solve
 
@@ -50,69 +50,4 @@ end
 
 
 
-"""
-    solve(problem)
-
-# Description
-
-Solves a portfolio optimization problem given by an object of in type `PortfolioProblem`.
-The optimization problem is formulated as a quadratic programming problem where the objective 
-is to minimize the portfolio variance (risk) subject to constraints on the expected return and the weights.
-
-Mathematically, the problem can be stated as:
-
-Minimize: w' * Σ * w
-Subject to:
-- sum(w) == 1 (the weights must sum to 1)
-- sum(w[i] * μ[i] for i in 1:m) >= thresholdreturn
-- 0 <= w[i] <= 1 for all i (weights must be between 0 and 1)
-
-Where:
-- w is the vector of asset weights
-- Σ is the covariance matrix of asset returns
-- μ is the vector of expected returns for each asset
-- thresholdreturn is the minimum expected return required for the portfolio
-
-# Arguments
-
-- `problem::PortfolioProblem`: The problem in type of PortfolioProblem
-
-# Returns
-
-- `PortfolioResult`: The result of the portfolio optimization problem, containing the optimal weights, expected return, and the JuMP model used to solve the problem.
-
-"""
-function solve(p::PortfolioProblem)::PortfolioResult
-    
-    model = Model(Ipopt.Optimizer)
-
-    MOI.set(model, MOI.Silent(), true)
-
-    means = mean(p.returns, dims = 1)
-
-    covmat = cov(p.returns)
-
-    _, m = size(p.returns)
-
-    @variable(model, 0 <= w[1:m] <= 1)
-    
-    @constraint(model, sum(w) == 1)
-
-    @constraint(model, sum(w[i] * means[i] for i in 1:m) >= p.thresholdreturn)
-
-    @objective(model, Min, w' * covmat * w)
-
-    optimize!(model)
-
-    weights = value.(w)
-
-    expectedreturn = sum(weights[i] * means[i] for i in 1:m)
-
-    return PortfolioResult(weights, expectedreturn, model)
-
-end 
-
-
-
-
 end # end of module

test/Project.toml

@@ -1,2 +1,3 @@
 [deps]
+Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

test/testportfolio.jl

@@ -1,3 +1,5 @@
+using Ipopt
+
 @testset "Portfolio Optimization" begin
     @testset "Markowitz Portfolio Optimization - Example 1" begin
         eps = 1e-5