remove Ipopt from weak dependencies and use HiGHS instead, in Portfolio Problems

Author: jbytecode

CHANGELOG.md

@@ -1,5 +1,6 @@
 ### 0.2.12 (Upcoming Release)
 
+- Remove Ipopt from weak dependencies and use HiGHS instead in Portfolio problems.
 
 ### 0.2.11 
 

Project.toml

@@ -9,11 +9,6 @@ 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"

docs/Project.toml

@@ -1,3 +1,2 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
-Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"

docs/make.jl

@@ -1,5 +1,4 @@
 using Documenter, OperationsResearchModels
-using Ipopt
 
 makedocs(
     format = Documenter.HTML(

ext/OperationsResearchModelsIpoptExt.jl

@@ -45,10 +45,11 @@ Defines the knapsack problem.
 # Output
 - `KnapsackResult`: The custom data type that holds selected items, model, and objective value.
 """
-struct KnapsackProblem
-    values::Vector{Float64}
-    weights::Vector{Float64}
-    capacity::Float64
+struct KnapsackProblem{T <: Real}
+    values::Vector{T}
+    weights::Vector{T}
+    capacity::T
+    
 end
 
 

src/knapsack.jl

@@ -1,6 +1,6 @@
 module Portfolio
 
-using JuMP, Statistics, LinearAlgebra
+using JuMP, HiGHS, Statistics, LinearAlgebra
 
 import ..OperationsResearchModels: solve
 
@@ -50,4 +50,70 @@ 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' * 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
+
+
+# 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.
+
+# Example
+```julia
+using OperationsResearchModels
+
+problem = PortfolioProblem(rand(100, 5), 0.01)
+result = solve(problem)
+println("Optimal Weights: ", result.weights)
+println("Expected Return: ", result.expectedreturn)
+```
+"""
+function solve(p::PortfolioProblem)::PortfolioResult
+    model = Model(HiGHS.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

src/portfolio.jl

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

test/Project.toml

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