朱莉娅中的NonlinearConstraintIndex是什么? [英] what is NonlinearConstraintIndex in julia?
问题描述
我厌倦了在以下代码中更改非线性约束的右手.尽管善良的人为我提供了很多帮助，但我找不到应该如何解决的方法.你能再帮我一次吗?非常感谢.
I tired to change right hand of non-linear constraint in the following code. although kind people helped me a lot, I couldn't to find how should I fix it. would you please help me again? Thank so much.
using JuMP, Ipopt, Juniper,Gurobi,CPUTime
#-----Model parameters--------------------------------------------------------
sig=0.86;
landa=50;
E=T0=T1=.0833;
T2=0.75;
gam2=1; gam1=0;
a1=5; a2=4.22; a3=977.4; ap=977.4;
C1=949.2; c0=114.24;
f(x) = cdf(Normal(0, 1), x);
#---------------------------------------------------------------------------
ALT= Model(optimizer_with_attributes(Juniper.Optimizer, "nl_solver"=>optimizer_with_attributes(Ipopt.Optimizer, "print_level" => 0),
"mip_solver"=>optimizer_with_attributes(Gurobi.Optimizer, "logLevel" => 0),"registered_functions" =>[Juniper.register( :f, 1, f; autodiff = true)])
);
# variables-----------------------------------------------------------------
JuMP.register(ALT, :f, 1, f; autodiff = true);
@variable(ALT, h >= 0.1);
@variable(ALT, L >= 0.00001);
@variable(ALT, n>=2, Int);
#---------------------------------------------------------------------------
@NLexpression(ALT,k1,h/(1-f(L-sig*sqrt(n))+f(-L - sig*sqrt(n))));
@NLexpression(ALT,k2,(1-(1+landa*h)*exp(-landa*h))/(landa*(1-exp(-landa*h))));
@NLexpression(ALT,k3,E*n+T1*gam1+T2*gam2);
@NLexpression(ALT,k4,1/landa+h/(1-f(L-sig*sqrt(n))+f(-L-sig*sqrt(n))));
@NLexpression(ALT,k5,-(1-(1+landa*h)*exp(-landa*h))/(landa*(1-exp(-landa*h)))+E*n+T1*gam1+T2*gam2);
@NLexpression(ALT,k6,(exp(-landa*h)/1-exp(-landa*h))*(a3/(2*f(-L)))+ap);
@NLexpression(ALT,k7,1-f(L-sig*sqrt(n))+f(-L-sig*sqrt(n)));
@NLexpression(ALT,F,c0/landa+C1*(k1-k2+k3)+((a1+a2*n)/h)*(k4+k5+k3)+k6);
@NLexpression(ALT,FF,k4-k2+E*n+T1+T2+(1-gam1)*((exp(-landa*h)/1-exp(-landa*h)*T0)/(2*f(-L))));
#routing constraints--------------------------------------------------------
@NLconstraint(ALT, f(-L) <= 1/400);
#objective function---------------------------------------------------------
@NLexpression(ALT,f1,F/FF);
@NLexpression(ALT,f2,1/k7);
#-------------------------------------------------------------------------
@NLparameter(ALT, rp1 == 10000);
@NLparameter(ALT, lp1 == -10000);
@NLparameter(ALT, rp2 == 10000);
@NLparameter(ALT, lp2 == -10000);
@NLconstraint(ALT,rf1,f1<=rp1);
@NLconstraint(ALT,lf1,f1>=lp1);
@NLconstraint(ALT,rf2,f2<=rp2);
@NLconstraint(ALT,lf2,f2>=lp2);
#------------------------------------------------------------------------
ZT=zeros(2,1);
ZB=zeros(2,1);
#-----------------------------------------------------------------------------
@NLobjective(ALT,Min,f2);
optimize!(ALT);
f2min=getvalue(f2);
ZB[2]=f2min;
set_value(rp2, f2min);
set_value(lp2, f2min);
@NLobjective(ALT,Min,f1);
optimize!(ALT);
ZB[1]=getvalue(f1);
#--------------------------------------------------------------------------
set_value(rp2, 10000);
set_value(lp2, ZB[2]+0.1);**
@NLobjective(ALT,Min,f1);
optimize!(ALT);
f1min=getvalue(f1);
ZT[1]=f1min;
尽管约束(**)限制了达到ZB(第二个目标优化时的目标值)，但第一个目标优化时却获得 949.2000589366443 .您能帮我什么原因吗?选择求解器可以有效吗?这些求解器不能解决非线性模型?非常感谢
although the constraint (**) limits getting to ZB (objective values when second objective optimized), it gets 949.2000589366443 when the first objective optimized. would you please help me what are the reasons?
is choosing solvers can be effective?
is the non-linear model cant be solve with these solvers?
Thank you very much
julia> ZB
2×1 Array{Float64,2}:
949.2000092739842
1.0000000053425355
#--------------------------------------------------
julia> ZT
2×1 Array{Float64,2}:
949.2000589366443
0.0
代码已更新.实际上，这段代码试图找到pareto front的两个点.这是一个例子
the code is updated. in fact, this code is trying to find two points of pareto front. this is an example
using JuMP,CPLEX,CPUTime
#----------------------------------------------------------------------
WES=Model(CPLEX.Optimizer)
#-----------------------------------------------------------------------
@variable(WES,x[i=1:4]>=0);
@variable(WES,y[i=5:6]>=0,Int);
@variable(WES,xp[i=1:4]>=0);
@variable(WES,yp[i=5:6]>=0,Int);
#-----------------------------------------------------------------------
ofv1=[3 6 -3 -5]
ofv2=[-15 -4 -1 -2];
f1=sum(ofv1[i]*x[i] for i=1:4);
f2=sum(ofv2[i]*x[i] for i=1:4);
f1p=sum(ofv1[i]*xp[i] for i=1:4);
f2p=sum(ofv2[i]*xp[i] for i=1:4);
#------------------------------------------------------------------------
@constraint(WES,con1,-x[1]+3y[5]<=0);
@constraint(WES,con2,x[1]-6y[5]<=0);
@constraint(WES,con3,-x[2]+3y[5]<=0);
@constraint(WES,con4,x[2]-6y[5]<=0);
@constraint(WES,con5,-x[3]+4y[6]<=0);
@constraint(WES,con6,x[3]-4.5y[6]<=0);
@constraint(WES,con7,-x[4]+4y[6]<=0);
@constraint(WES,con8,x[4]-4.5y[6]<=0);
@constraint(WES,con9,y[5]+y[6]<=5);
@constraint(WES,con14,-xp[1]+3yp[5]<=0);
@constraint(WES,con15,xp[1]-6yp[5]<=0);
@constraint(WES,con16,-xp[2]+3yp[5]<=0);
@constraint(WES,con17,xp[2]-6yp[5]<=0);
@constraint(WES,con18,-xp[3]+4yp[6]<=0);
@constraint(WES,con19,xp[3]-4.5yp[6]<=0);
@constraint(WES,con20,-xp[4]+4yp[6]<=0);
@constraint(WES,con21,xp[4]-4.5yp[6]<=0);
@constraint(WES,con22,yp[5]+yp[6]<=5);
#------------------------------------------------------------------------
ZT=zeros(2,1);
ZB=zeros(2,1);
#--------------------------------------------------------------------------------
@objective(WES,Min,f2);
optimize!(WES);
f2min=JuMP.value(f2)
set_normalized_rhs(rf2,f2min);
set_normalized_rhs(lf2,f2min);
ZB[2]=getvalue(f2);
@objective(WES,Min,f1);
optimize!(WES);
ZB[1]=getvalue(f1);
#----------------
JuMP.setRHS(rf2,10000);
JuMP.setRHS(lf2,ZB[2]);
@objective(WES,Min,f1);
optimize!(WES);
set_normalized_rhs(rf1,getvalue(f1));
set_normalized_rhs(lf1,getvalue(f1));
ZT[1]=getvalue(f1);
@objective(WES,Min,f2);
optimize!(WES);
ZT[2]=getvalue(f2);
但是当右侧功能运行时，它再次出现该错误.
but it has that error again when the right hand sides functions are run.
set_normalized_rhs(rf2,f2min)
ERROR: MethodError: no method matching set_normalized_rhs(::ConstraintRef{Model,NonlinearConstraintIndex,ScalarShape}, ::Float64)
Closest candidates are:
set_normalized_rhs(::ConstraintRef{Model,MathOptInterface.ConstraintIndex{F,S},Shape} where Shape<:AbstractShape, ::Any) where {T, S<:Union{MathOptInterface.EqualTo{T}, MathOptInterface.GreaterThan{T}, MathOptInterface.LessThan{T}}, F<:Union{MathOptInterface.ScalarAffineFunction{T}, MathOptInterface.ScalarQuadraticFunction{T}}} at C:\Users\admin\.julia\packages\JuMP\YXK4e\src\constraints.jl:478
Stacktrace:
[1] top-level scope at none:1
我找不到问题所在.此示例在Julia 0.6.4.2中运行.ZB和ZT是:
I cant find what is the problem. this example was run in Julia 0.6.4.2. ZB and ZT were:
julia>ZB
2×1 Array{Float64,2}:
270.0
-570.0
julia> ZT
2×1 Array{Float64,2}:
-180.0
-67.5.0
的确感谢.
推荐答案
Duplicate of is there any possibility to change the RHS of non-linear constraints in julia?.
您可以使用 set_value 更新非线性参数的值. https://jump.dev/JuMP.jl/v0.21.3/nlp/#JuMP.set_value-Tuple{NonlinearParameter,Number}
You can use set_value to update the value of a nonlinear parameter. https://jump.dev/JuMP.jl/v0.21.3/nlp/#JuMP.set_value-Tuple{NonlinearParameter,Number}
这是一个例子
using JuMP
model = Model()
@variable(model, x)
@NLparameter(model, p == 1)
@NLconstraint(model, sqrt(x) <= p)
# To make RHS p=2
set_value(p, 2)
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