diff --git a/examples/explicit_kinetic_problem.jl b/examples/explicit_kinetic_problem.jl
index 3f44aaa..41f3232 100644
--- a/examples/explicit_kinetic_problem.jl
+++ b/examples/explicit_kinetic_problem.jl
@@ -9,9 +9,13 @@
 # Import the necessary packages
 using EAGO, JuMP, CSV, DataFrames, Symbolics, SourceCodeMcCormick, CUDA, BenchmarkTools
 
+# Import the ParBB algorithm
+include(joinpath(@__DIR__, "ParBB", "extension.jl"))
+include(joinpath(@__DIR__, "ParBB", "subroutines.jl"))
+
 # Import the kinetic intensity data
-data = CSV.read("C:/Users/rxg20001/Documents/Github_Repositories/EAGO-GPU.jl/kinetic_intensity_data.csv", DataFrame)
-bounds = CSV.read("C:/Users/rxg20001/Documents/Github_Repositories/EAGO-GPU.jl/implicit_variable_bounds.csv", DataFrame)
+data = CSV.read(joinpath(@__DIR__, "kinetic_intensity_data.csv"), DataFrame)
+bounds = CSV.read(joinpath(@__DIR__, "implicit_variable_bounds.csv"), DataFrame)
 
 # Define the constant terms in the expressions
 T = 273.0
diff --git a/examples/implicit_variable_bounds.csv b/examples/implicit_variable_bounds.csv
new file mode 100644
index 0000000..c2bd71a
--- /dev/null
+++ b/examples/implicit_variable_bounds.csv
@@ -0,0 +1,1002 @@
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diff --git a/examples/kinetic_intensity_data.csv b/examples/kinetic_intensity_data.csv
new file mode 100644
index 0000000..fd56fb5
--- /dev/null
+++ b/examples/kinetic_intensity_data.csv
@@ -0,0 +1,201 @@
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+1.22,18.1952
+1.23,17.8476
+1.24,17.9095
+1.25,17.5048
+1.26,17.5
+1.27,15.9619
+1.28,16.2095
+1.29,16.181
+1.3,15.6952
+1.31,15.7095
+1.32,15.4619
+1.33,15.9476
+1.34,16
+1.35,16.1952
+1.36,16.1143
+1.37,15.7429
+1.38,15.5762
+1.39,15.7048
+1.4,15.8095
+1.41,15.6667
+1.42,14.9048
+1.43,14.5857
+1.44,14.7524
+1.45,14.7571
+1.46,14.9762
+1.47,14.5333
+1.48,14.5524
+1.49,14.0143
+1.5,13.6286
+1.51,13.4429
+1.52,13.4667
+1.53,13.319
+1.54,12.9333
+1.55,13.1238
+1.56,12.7476
+1.57,12.9333
+1.58,13.0714
+1.59,13.0714
+1.6,12.7619
+1.61,12.4238
+1.62,12.5143
+1.63,12.9143
+1.64,12.5714
+1.65,13.3667
+1.66,13.2286
+1.67,13.7905
+1.68,13.7571
+1.69,13.5905
+1.7,12.9667
+1.71,12.981
+1.72,12.8857
+1.73,12.919
+1.74,13.0143
+1.75,13.0095
+1.76,12.3857
+1.77,12.5571
+1.78,12.3429
+1.79,12.7571
+1.8,12.681
+1.81,12.5429
+1.82,12.1857
+1.83,12.7905
+1.84,12.5571
+1.85,12.8429
+1.86,12.5476
+1.87,12.5714
+1.88,12.3762
+1.89,11.9952
+1.9,11.4571
+1.91,11.3
+1.92,11.1524
+1.93,11.681
+1.94,11.619
+1.95,11.9048
+1.96,12
+1.97,12.0762
+1.98,11.9143
+1.99,11.7619
+2,11.5333
diff --git a/examples/polynomial_pressure_data.csv b/examples/polynomial_pressure_data.csv
new file mode 100644
index 0000000..01d594e
--- /dev/null
+++ b/examples/polynomial_pressure_data.csv
@@ -0,0 +1,51 @@
+pressure,w,T
+12.38,0.4992,333.15
+10.1,0.5991,333.15
+6.31,0.6997,333.15
+28.33,0.4994,353.15
+24.17,0.6,353.15
+18,0.7,353.15
+12.38,0.7493,353.15
+60.13,0.4997,373.15
+48.49,0.6006,373.15
+36.46,0.7003,373.15
+26.54,0.7495,373.15
+20.32,0.8,373.15
+13.61,0.8503,373.15
+118.57,0.5004,393.15
+92.9,0.6015,393.15
+70.48,0.7008,393.15
+53.49,0.75,393.15
+40.23,0.8004,393.15
+27.84,0.8506,393.15
+215.95,0.5014,413.15
+179.25,0.6033,413.15
+126.59,0.7017,413.15
+100.39,0.7508,413.15
+78.16,0.8011,413.15
+52.82,0.8511,413.15
+33.67,0.8998,413.15
+377.13,0.503,433.15
+292.92,0.6054,433.15
+220.38,0.7031,433.15
+175.32,0.752,433.15
+140.91,0.8022,433.15
+93.25,0.8519,433.15
+59.32,0.9003,433.15
+38.36,0.9501,433.15
+596.57,0.505,453.15
+496.87,0.6092,453.15
+361.58,0.7051,453.15
+288.22,0.7537,453.15
+229.18,0.8036,453.15
+159.01,0.8531,453.15
+101.2,0.9011,453.15
+63.43,0.9506,453.15
+920.02,0.5079,473.15
+789.6,0.6145,473.15
+564.39,0.7078,473.15
+451.19,0.756,473.15
+357.82,0.8056,473.15
+249.55,0.8547,473.15
+162.51,0.9022,473.15
+99.24,0.9512,473.15
diff --git a/examples/polynomial_pressure_problem.jl b/examples/polynomial_pressure_problem.jl
index 4505042..8888d4b 100644
--- a/examples/polynomial_pressure_problem.jl
+++ b/examples/polynomial_pressure_problem.jl
@@ -12,6 +12,9 @@ using Symbolics, SourceCodeMcCormick, CUDA
 include(joinpath(@__DIR__, "ParBB", "extension.jl"))
 include(joinpath(@__DIR__, "ParBB", "subroutines.jl"))
 
+# Import the pressure data for the polynomial equation
+pressure_data = CSV.read(joinpath(@__DIR__, "polynomial_pressure_data.csv"), DataFrame)
+
 # Adjust CUDA settings to make sure no scalar calculations occur
 CUDA.allowscalar(false)
 
@@ -28,21 +31,11 @@ Symbolics.@variables calc, data
 SSE_cv, _ = convex_evaluator(((calc - data)/data)^2)
 
 # Set up the GPU-compatible L2norm calculation
-function L2norm(p...)
-    pset = [12.38, 10.10, 6.31, 28.33, 24.17, 18.00, 12.38, 60.13, 48.49, 36.46, 26.54, 20.32, 13.61, 
-            118.57, 92.90, 70.48, 53.49, 40.23, 27.84, 215.95, 179.25, 126.59, 100.39, 78.16, 52.82, 
-            33.67, 377.13, 292.92, 220.38, 175.32, 140.91, 93.25, 59.32, 38.36, 596.57, 496.87, 361.58,
-            288.22, 229.18, 159.01, 101.20, 63.43, 920.02, 789.60, 564.39, 451.19, 357.82, 249.55, 162.51, 99.24]
-    wset = [0.4992, 0.5991, 0.6997, 0.4994, 0.6000, 0.7000, 0.7493, 0.4997, 0.6006, 0.7003, 0.7495, 
-            0.8000, 0.8503, 0.5004, 0.6015, 0.7008, 0.7500, 0.8004, 0.8506, 0.5014, 0.6033, 0.7017,
-            0.7508, 0.8011, 0.8511, 0.8998, 0.5030, 0.6054, 0.7031, 0.7520, 0.8022, 0.8519, 0.9003, 
-            0.9501, 0.5050, 0.6092, 0.7051, 0.7537, 0.8036, 0.8531, 0.9011, 0.9506, 0.5079, 0.6145,
-            0.7078, 0.7560, 0.8056, 0.8547, 0.9022, 0.9512]
-    Tset = [333.15, 333.15, 333.15, 353.15, 353.15, 353.15, 353.15, 373.15, 373.15, 373.15, 373.15, 
-            373.15, 373.15, 393.15, 393.15, 393.15, 393.15, 393.15, 393.15, 413.15, 413.15, 413.15, 
-            413.15, 413.15, 413.15, 413.15, 433.15, 433.15, 433.15, 433.15, 433.15, 433.15, 433.15, 
-            433.15, 453.15, 453.15, 453.15, 453.15, 453.15, 453.15, 453.15, 453.15, 473.15, 473.15, 
-            473.15, 473.15, 473.15, 473.15, 473.15, 473.15]
+L2norm(p...) = L2norm(pressure_data, p...)
+function L2norm(pressure_data::DataFrame, p...)
+    pset = pressure_data.pressure
+    wset = pressure_data.w
+    Tset = pressure_data.T
     
     L = length(p[1])
     SSEcv = CUDA.zeros(Float64, L)
@@ -97,22 +90,12 @@ function L2norm(p...)
 end
 
 # CPU version of the function, to be used for IPOPT
-function L2norm_cpu(p1::T, p2::T, p3::T, p4::T, p5::T, p6::T) where {T<:Real}
+L2norm_cpu(p1::T, p2::T, p3::T, p4::T, p5::T, p6::T) where {T<:Real} = L2norm_cpu(pressure_data, p1, p2, p3, p4, p5, p6)
+function L2norm_cpu(pressure_data::DataFrame, p1::T, p2::T, p3::T, p4::T, p5::T, p6::T) where {T<:Real}
     SSE = zero(T)
-    pset = [12.38, 10.10, 6.31, 28.33, 24.17, 18.00, 12.38, 60.13, 48.49, 36.46, 26.54, 20.32, 13.61, 
-            118.57, 92.90, 70.48, 53.49, 40.23, 27.84, 215.95, 179.25, 126.59, 100.39, 78.16, 52.82, 
-            33.67, 377.13, 292.92, 220.38, 175.32, 140.91, 93.25, 59.32, 38.36, 596.57, 496.87, 361.58,
-            288.22, 229.18, 159.01, 101.20, 63.43, 920.02, 789.60, 564.39, 451.19, 357.82, 249.55, 162.51, 99.24]
-    wset = [0.4992, 0.5991, 0.6997, 0.4994, 0.6000, 0.7000, 0.7493, 0.4997, 0.6006, 0.7003, 0.7495, 
-            0.8000, 0.8503, 0.5004, 0.6015, 0.7008, 0.7500, 0.8004, 0.8506, 0.5014, 0.6033, 0.7017,
-            0.7508, 0.8011, 0.8511, 0.8998, 0.5030, 0.6054, 0.7031, 0.7520, 0.8022, 0.8519, 0.9003, 
-            0.9501, 0.5050, 0.6092, 0.7051, 0.7537, 0.8036, 0.8531, 0.9011, 0.9506, 0.5079, 0.6145,
-            0.7078, 0.7560, 0.8056, 0.8547, 0.9022, 0.9512]
-    Tset = [333.15, 333.15, 333.15, 353.15, 353.15, 353.15, 353.15, 373.15, 373.15, 373.15, 373.15, 
-            373.15, 373.15, 393.15, 393.15, 393.15, 393.15, 393.15, 393.15, 413.15, 413.15, 413.15, 
-            413.15, 413.15, 413.15, 413.15, 433.15, 433.15, 433.15, 433.15, 433.15, 433.15, 433.15, 
-            433.15, 453.15, 453.15, 453.15, 453.15, 453.15, 453.15, 453.15, 453.15, 473.15, 473.15, 
-            473.15, 473.15, 473.15, 473.15, 473.15, 473.15]
+    pset = pressure_data.pressure
+    wset = pressure_data.w
+    Tset = pressure_data.T
 
     for i = 1:length(pset)
         temp = exp(p1 + p2*wset[i] + p3*wset[i]^2 + (1/Tset[i])*(p4 + p5*wset[i] + p6*wset[i]^2))