I can write in function in Matlab in this way:

function res=resid(theta,alpha,beta);

RHS=;

LHS=;

RHS= theta-alpha;

LHS= theta*beta;

res = (LHS-RHS);

We set the parameters, call the function:

alpha=0.3;beta=0.95;

a01=[1.0;1.0];

th=fsolve(‘resid’,a01,,alpha,beta)

This will return [6.0;6.0]. Does the option"" signal to fsolve that the input is a vector?

Anyway, how can I implement this in Julia using a NLsolve, Optim or JuMP? The original problem has more than 10 variables, so I would prefer a vector approach.

I can implement the function in Julia:

h! =function (theta)

RHS=;

LHS=;

RHS= theta-alpha;

LHS= theta*beta;

res= (LHS-RHS);

return res;

end

But simply using NLsolve:

a01 = [1.0;1.0];

res = nlsolve(h!,a01)

Returns:

MethodError: no method matching (::##17#18)(::Array{Float64,1}, ::Array{Float64,1})

Closest candidates are:

#17(::Any) at In[23]:3

If I alternatively use Optim, I get:

using Optim

optimize(h!, a01)

which returns:

MethodError: Cannot `convert`

an object of type Array{Float64,1} to an object of type Float64

This may have arisen from a call to the constructor Float64(…),

since type constructors fall back to convert methods.