朱莉娅中的NonlinearConstraintIndex是什么？

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，但我找不到解决方法。您能再帮我一次吗？非常感谢。

@H_404_2@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,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,f1); optimize!(ALT); ZB[1]=getvalue(f1); #-------------------------------------------------------------------------- set_value(rp2,10000); set_value(lp2,ZB[2]+0.1);** @NLobjective(ALT,f1); optimize!(ALT); f1min=getvalue(f1); ZT[1]=f1min;

尽管约束（**）限制了ZB（第二个目标优化时的目标值），但第一个目标优化时它的约束是949.2000589366443。您能帮我什么原因吗？ 选择求解器是否有效？ 这些求解器无法求解非线性模型？ 非常感谢

@H_404_2@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

@H_404_2@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); #-------------------------------------------------------------------------------- @objective(WES,f2); optimize!(WES); f2min=JuMP.value(f2) set_normalized_rhs(rf2,f2min); set_normalized_rhs(lf2,f2min); ZB[2]=getvalue(f2); @objective(WES,f1); optimize!(WES); ZB[1]=getvalue(f1); #---------------- JuMP.setRHS(rf2,10000); JuMP.setRHS(lf2,ZB[2]); @objective(WES,f1); optimize!(WES); set_normalized_rhs(rf1,getvalue(f1)); set_normalized_rhs(lf1,getvalue(f1)); ZT[1]=getvalue(f1); @objective(WES,f2); optimize!(WES); ZT[2]=getvalue(f2);

但是当运行右侧功能时，它再次出现该错误。

@H_404_2@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：

@H_404_2@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。

解决方法

与is there any possibility to change the RHS of non-linear constraints in julia?重复。

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}

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)