remove redundant constraints in LAD and Quantile Regression

Author: jbytecode

CHANGELOG.md

@@ -3,6 +3,8 @@
 - Update citation of `robhatreg` a.k.a Robust Hat Matrix based Regression Estimator
 - Fix typos in code, code comments, and documentation
 - Replace one dimensional `Array{` definitions with `Vector{`
+- Define deviations and their bounds in a single line and remove additional constraints in LAD and Quantile Regression
+
 
 # v0.11.5
 

src/lad.jl

@@ -106,7 +106,8 @@ function lad_exact(X::AbstractMatrix{Float64}, y::AbstractVector{Float64})
     m = JuMP.Model(HiGHS.Optimizer)
     set_silent(m)
 
-    JuMP.@variable(m, d[1:(2n)])
+    # d[i] >= 0 for i = 1, ..., 2n
+    JuMP.@variable(m, d[1:(2n)] .>= 0)  
     JuMP.@variable(m, beta[1:p])
 
     JuMP.@objective(m, Min, sum(d[i] for i = 1:(2n)))
@@ -115,10 +116,6 @@ function lad_exact(X::AbstractMatrix{Float64}, y::AbstractVector{Float64})
         c = JuMP.@constraint(m, y[i] - sum(X[i, :] .* beta) + d[i] - d[n+i] == 0)
     end
 
-    for i = 1:(2n)
-        JuMP.@constraint(m, d[i] >= 0)
-    end
-
     JuMP.optimize!(m)
 
     betahats = JuMP.value.(beta)

src/quantileregression.jl

@@ -86,7 +86,8 @@ function quantileregression(X::AbstractMatrix{Float64}, y::AbstractVector{Float6
     m = JuMP.Model(HiGHS.Optimizer)
     set_silent(m)
 
-    JuMP.@variable(m, d[1:(2n)])
+    # d[i] > 0 for i = 1:2n
+    JuMP.@variable(m, d[1:(2n)] .>= 0)
     JuMP.@variable(m, beta[1:p])
 
     JuMP.@objective(
@@ -99,10 +100,6 @@ function quantileregression(X::AbstractMatrix{Float64}, y::AbstractVector{Float6
         _ = JuMP.@constraint(m, y[i] - sum(X[i, :] .* beta) + d[i] - d[n+i] == 0)
     end
 
-    for i = 1:(2n)
-        JuMP.@constraint(m, d[i] >= 0)
-    end
-
     JuMP.optimize!(m)
 
     betahats = JuMP.value.(beta)