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HW3 Guillard & Ghio #5

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HW3 Guillard & Ghio #5

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92f7e11
my homework
nicolasghio Mar 31, 2019
c1b2345
my homework
nicolasghio Mar 31, 2019
67444bb
my homework
nicolasghio Mar 31, 2019
afef4ab
my homework
nicolasghio Mar 31, 2019
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7 changes: 7 additions & 0 deletions Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,13 @@ uuid = "2aa259a0-4d4c-11e9-0909-ad9b1ef308f5"
authors = ["florian oswald <[email protected]>"]
version = "0.1.0"

[deps]
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"
JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
NLopt = "76087f3c-5699-56af-9a33-bf431cd00edd"

[extras]
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

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188 changes: 76 additions & 112 deletions src/HWconstrained.jl
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@@ -1,117 +1,81 @@
module HWconstrained

greet() = print("Hello World!")

using JuMP, NLopt, DataFrames, Ipopt
using LinearAlgebra

export data, table_NLopt, table_JuMP

function data(a=0.5)









return Dict("a"=>a,"na"=>na,"nc"=>nc,"ns"=>ns,"nss"=>nss,"e"=>e,"p"=>p,"z"=>z,"pi"=>pi)
end


function max_JuMP(a=0.5)

return Dict("obj"=>objective_value(m),"c"=>value(c),"omegas"=>[value(omega[i]) for i in 1:length(omega)])
end

function table_JuMP()
d = DataFrame(a=[0.5;1.0;5.0],c = zeros(3),omega1=zeros(3),omega2=zeros(3),omega3=zeros(3),fval=zeros(3))
for i in 1:nrow(d)
xx = max_JuMP(d[i,:a])
d[i,:c] = xx["c"]
d[i,:omega1] = xx["omegas"][1]
d[i,:omega2] = xx["omegas"][2]
d[i,:omega3] = xx["omegas"][3]
d[i,:fval] = xx["obj"]
end
return d
end








function obj(x::Vector,grad::Vector,data::Dict)
























end

function constr(x::Vector,grad::Vector,data::Dict)














end

function max_NLopt(a=0.5)








end

function table_NLopt()
d = DataFrame(a=[0.5;1.0;5.0],c = zeros(3),omega1=zeros(3),omega2=zeros(3),omega3=zeros(3),fval=zeros(3))
for i in 1:nrow(d)
xx = max_NLopt(d[i,:a])
for j in 2:ncol(d)-1
d[i,j] = xx[2][j-1]
end
d[i,end] = xx[1]
end
return d
end
using JuMP, NLopt, DataFrames, Ipopt
using LinearAlgebra
export data, table_NLopt, table_JuMP

### Question 1
function data(a=0.5)
n=3
p=[1, 1, 1]
e=[2, 0, 0]
s1=s2=4
z1=[1, 1, 1, 1]
z2=[0.72, 0.92, 1.12, 1.32]
z3=[0.86, 0.96, 1.06, 1.16]
z=[[1, i, j] for i in z2 for j in z3]
z = vcat(z'...)
pi = repeat([1/16], 16)
a=0.5
na=3
nc=4
ns=4
nss=16

return Dict("a"=>a,"na"=>na,"nc"=>nc,"ns"=>ns,"nss"=>nss,"e"=>e,"p"=>p,"z"=>z,"pi"=>pi)
end
#end

d=data()

#### Question 2

function obj(x::Vector,grad::Vector,data::Dict)
A = data["a"]
Z = data["z"]
pi = data["pi"]
if length(grad) > 0
grad[1] = A*exp.(-A*x[1])
for i in 1:3
grad[i+1] = sum(pi .* Z[:,i] .*A.*exp.(-A.*Z*x[i+1]))
end
end
return -exp.(-A*x[1])+ sum(pi.*-exp.(-A*Z*x[2:4]))
end
obj(ones(4), zeros(4), d)

function constr(x::Vector,grad::Vector,data::Dict)
if length(grad) > 0
grad[1] = d["a"]*exp(-d["a"]*x[1])
grad[2:end] = d["p"]
end
return x[1] + sum(d["p"].*(x[2:end].-d["e"]))
end

constr(ones(4), zeros(4), d) # keep track of # function evaluations

function max_NLopt(a=0.5)
d = data(a)
e= d["e"]
optimum = Opt(:LD_MMA, 4)
lower_bounds!(optimum, [0., -Inf, -Inf, -Inf])
max_objective!(optimum, (x, g)->obj(x, g, d))
inequality_constraint!(optimum, (x, g)->constr(x, g, d), 1e-8)
ftol_rel!(optimum, 1e-8)
NLopt.optimize(optimum, vcat(0,e))
end

function table_NLopt()
d = DataFrame(a=[0.5;1.0;5.0],c = zeros(3),omega1=zeros(3),omega2=zeros(3),omega3=zeros(3),fval=zeros(3))
for i in 1:nrow(d)
xx = max_NLopt(d[i,:a])
for j in 2:ncol(d)-1
d[i,j] = xx[2][j-1]
end
d[i,end] = xx[1]
end
return d
end



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97 changes: 50 additions & 47 deletions test/runtests.jl
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Expand Up @@ -2,52 +2,55 @@ using HWconstrained
using Test

@testset "HWconstrained.jl" begin
@testset "testing components" begin

@testset "tests gradient of objective function" begin






end


@testset "tests gradient of constraint function" begin






end
end

@testset "testing result of both maximization methods" begin

truth = HWconstrained.DataFrame(a=[0.5;1.0;5.0],
c = [1.008;1.004;1.0008],
omega1=[-1.41237;-0.20618;0.758763],
omega2=[0.801455;0.400728;0.0801455],
omega3=[1.60291;0.801455;0.160291],
fval=[-1.20821;-0.732819;-0.013422])

@testset "checking result of NLopt maximization" begin

t1 = table_NLopt()
for c in names(truth)
@test all(maximum.(abs.(t1[c].-truth[c])) .< tol2)
end
end


@testset "checking result of NLopt maximization" begin
t1 = table_JuMP()
for c in names(truth)
@test all(maximum.(abs.(t1[c].-truth[c])) .< tol2)
end
end
end

@testset "testing components" begin
d=data()
A=d["a"]
u(x)=-exp(-A*x)
grad=ones(4)


@testset "tests gradient of objective function" begin

end





@testset "tests gradient of constraint function" begin

end

end



@testset "testing result of both maximization methods" begin
truth = HWconstrained.DataFrame(a=[0.5;1.0;5.0],

c = [1.008;1.004;1.0008],

omega1=[-1.41237;-0.20618;0.758763],

omega2=[0.801455;0.400728;0.0801455],

omega3=[1.60291;0.801455;0.160291],

fval=[-1.20821;-0.732819;-0.013422])

tol2=1e-2

@testset "checking result of NLopt maximization" begin
t1 = table_JuMP()
for c in names(truth)
@test all(maximum.(abs.(t1[c].-truth[c])) .< tol2)
end
end

@testset "checking result of NLopt maximization" begin
@test all(maximum.(abs.(t1[c].-truth[c])) .< tol2)
end
end
end
end