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HW_Xiaojie Liu #3

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61b6a9b
HW_Xiaojie Liu
Zoro20171002 Mar 30, 2019
696e981
HW_test
Zoro20171002 Apr 1, 2019
9803a82
HW_Xiaojie
Zoro20171002 Apr 1, 2019
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225 changes: 107 additions & 118 deletions src/HWconstrained.jl
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Original file line number Diff line number Diff line change
@@ -1,118 +1,107 @@
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



end # module
module HWconstrained

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

using JuMP, NLopt, DataFrames, Ipopt
using LinearAlgebra

export data, table_NLopt, table_JuMP

function data(a=0.5)
na = 3
nc = 1
ns = 4
nss = 16
e = [2,0,0]
p = [1,1,1]
z_2 = [0.72,0.92,1.12,1.32]
z_3 = [0.86,0.96,1.06,1.16]
z = [[1.0,z_2[j],z_3[k]] for j in 1:4 for k in 1:4]
pi = 1/16*ones(1,16)
return Dict("a"=>a,"na"=>na,"nc"=>nc,"ns"=>ns,"nss"=>nss,"e"=>e,"p"=>p,"z"=>z,"pi"=>pi)
end

function maxJuMP(a=0.5)
d = data(a)
n = Model(with_optimizer(Ipopt.Optimizer))
@variable(n, omega[1:3])
@variable(n, c)
@NLobjective(n, Min, exp(-a*c)+sum(d["pi"][i]*exp((-a)*sum(d["z"][i][j]'*omega[j] for j in 1:d["na"])) for i in 1:d["nss"]))
@constraint(n, c + d["p"]'*(omega - d["e"]) <= 0)
JuMP.optimize!(n)
return Dict("obj"=>objective_value(n),"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 = maxJuMP(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,d::Dict)
a = d["a"]
z = d["z"]
pi = d["pi"]
if length(grad)>0
grad[1] = sum(d["pi"][i]*(-a)*exp((-a)*z[i][1]*sum(z[i]'*x[1:3])) for i in 1:d["nss"])
grad[2] = sum(d["pi"][i]*(-a)*exp((-a)*z[i][2]*sum(z[i]'*x[1:3])) for i in 1:d["nss"])
grad[3] = sum(d["pi"][i]*(-a)*exp((-a)*z[i][3]*sum(z[i]'*x[1:3])) for i in 1:d["nss"])
grad[4] = (-a)*exp.(-a*x[4])
end
return (exp(-a*x[4])+sum(d["pi"][i]*exp((-a)*sum(z[i]'*x[1:3])) for i in 1:d["nss"]))
end


function constr(x::Vector,grad::Vector,d::Dict)
if length(grad)>0
grad[1] = d["p"][1]
grad[2] = d["p"][2]
grad[3] = d["p"][3]
grad[4] = 1
end
return x[4] + d["p"]'*(x[1:3] - d["e"])
end







function max_NLopt(a=0.5)
count = 0 # keep track of # function evaluations
opt = Opt(:LD_MMA, 4)
lower_bounds!(opt, [-Inf, -Inf, -Inf, 0.])
xtol_rel!(opt, 1e-7)
grad = zeros(4)
println(length(grad))
d = data(a)
min_objective!(opt,(x,grad) -> obj(x,grad,d))
inequality_constraint!(opt, (x,grad) -> constr(x,grad,d))
ftol_rel!(opt,1e-9)
NLopt.optimize(opt, [0,0,0,0])
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



end # module
107 changes: 54 additions & 53 deletions test/runtests.jl
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Original file line number Diff line number Diff line change
@@ -1,53 +1,54 @@
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

end
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
tol2=1e-2
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
tol2=1e-2
t1 = table_JuMP()
for c in names(truth)
@test all(maximum.(abs.(t1[c].-truth[c])) .< tol2)
end
end
end

end