# Using optimization parameters in ModelingToolkit
**URL:** https://discourse.julialang.org/t/using-optimization-parameters-in-modelingtoolkit/82099
**Category:** Modelling & Simulations
**Tags:** modelingtoolkit
**Created:** [June 2, 2022, 1:54am UTC](https://discourse.julialang.org/t/using-optimization-parameters-in-modelingtoolkit/82099 "2022-06-02T01:54:08Z")
**Posts on this page:** 13
**Page:** 1
### Author:  [@mzhenirovskyy](https://discourse.julialang.org/u/mzhenirovskyy)
#### Post date: [June 2, 2022, 1:54am UTC](https://discourse.julialang.org/t/using-optimization-parameters-in-modelingtoolkit/82099/1 "2022-06-02T01:54:08Z")
Hi All,
I want to be able to pass optimization parameters to the model.
I took the RC circuit from the docs and created 2 parameters ( **param\_r1 param\_c1** )
[https://mtk.sciml.ai/stable/tutorials/acausal\_components/#Connecting-and-Simulating-Our-Electrical-Circuit](https://mtk.sciml.ai/stable/tutorials/acausal_components/#Connecting-and-Simulating-Our-Electrical-Circuit)
My code:
```julia
using ModelingToolkit, DifferentialEquations
using ModelingToolkitStandardLibrary.Electrical
@variables t
@parameters param_r1 param_c1
@named resistor = Resistor(R=param_r1)
@named capacitor = Capacitor(C=param_c1)
@named source = ConstantVoltage(V=1.)
@named ground = Ground()
rc_eqs = [
connect(source.p, resistor.p)
connect(resistor.n, capacitor.p)
connect(capacitor.n, source.n)
connect(capacitor.n, ground.g)
]
@named _rc_model = ODESystem(rc_eqs, t)
@named rc_model = compose(_rc_model,
[resistor, capacitor, source, ground])
sys = structural_simplify(rc_model)
u0 = [
capacitor.v => 0.0
]
params = [param_r1 => 1.0, param_c1 => 1.0]
tspan = (0., 10.)
prob = ODAEProblem(sys, u0, tspan, params)
```
The last line gives me the next error:
```julia
ERROR: MethodError: no method matching float(::Type{Any})
Closest candidates are:
float(::Type{Union{Missing, T}}) where T at C:\Users\mzhen\AppData\Local\Programs\Julia-1.7.2\share\julia\base\missing.jl:112
float(::Any) at C:\Users\mzhen\AppData\Local\Programs\Julia-1.7.2\share\julia\base\float.jl:269
float(::Union{StatsBase.PValue, StatsBase.TestStat}) at C:\Users\mzhen\.julia\packages\StatsBase\pJqvO\src\statmodels.jl:86
...
Stacktrace:
[1] float(#unused#::Type{Any})
@ Base .\missing.jl:113
[2] promote_to_concrete(vs::Vector{Any}; tofloat::Bool, use_union::Bool)
@ ModelingToolkit C:\Users\mzhen\.julia\packages\ModelingToolkit\LXLjs\src\utils.jl:558
[3] varmap_to_vars(varmap::Vector{Pair{Num, Float64}}, varlist::Vector{Sym{Real, Base.ImmutableDict{DataType, Any}}}; defaults::Dict{Any, Any}, check::Bool, toterm::Function, promotetoconcrete::Nothing, tofloat::Bool, use_union::Bool)
@ ModelingToolkit C:\Users\mzhen\.julia\packages\ModelingToolkit\LXLjs\src\variables.jl:68
[4] ODAEProblem{true}(sys::ODESystem, u0map::Vector{Pair{Num, Float64}}, tspan::Tuple{Float64, Float64}, parammap::Vector{Pair{Num, Float64}}; callback::Nothing, use_union::Bool, check::Bool, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
@ ModelingToolkit.StructuralTransformations C:\Users\mzhen\.julia\packages\ModelingToolkit\LXLjs\src\structural_transformation\codegen.jl:523
[5] ODAEProblem{true}(sys::ODESystem, u0map::Vector{Pair{Num, Float64}}, tspan::Tuple{Float64, Float64}, parammap::Vector{Pair{Num, Float64}})
@ ModelingToolkit.StructuralTransformations C:\Users\mzhen\.julia\packages\ModelingToolkit\LXLjs\src\structural_transformation\codegen.jl:514
[6] ODAEProblem(::ODESystem, ::Vararg{Any}; kw::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
@ ModelingToolkit.StructuralTransformations C:\Users\mzhen\.julia\packages\ModelingToolkit\LXLjs\src\structural_transformation\codegen.jl:492
[7] ODAEProblem(::ODESystem, ::Vararg{Any})
@ ModelingToolkit.StructuralTransformations C:\Users\mzhen\.julia\packages\ModelingToolkit\LXLjs\src\structural_transformation\codegen.jl:492
[8] top-level scope
@ d:\Projects\playground\julia\grid\src\grid _stl.jl:31
```
How to fix it?
Thanks!
---
### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [June 2, 2022, 10:20am UTC](https://discourse.julialang.org/t/using-optimization-parameters-in-modelingtoolkit/82099/2 "2022-06-02T10:20:09Z")
@YingboMa this looks like a bug?
---
### Author:  [@YingboMa](https://discourse.julialang.org/u/YingboMa)
#### Post date: [June 2, 2022, 8:07pm UTC](https://discourse.julialang.org/t/using-optimization-parameters-in-modelingtoolkit/82099/3 "2022-06-02T20:07:35Z")
Don’t use new parameters like `param_r1` and `param_c1` while `resistor.R` and `capacitor.R` already parameters. MTK doesn’t know `param_r1` and `param_c1` are parameters of the model.
---
### Author:  [@mzhenirovskyy](https://discourse.julialang.org/u/mzhenirovskyy)
#### Post date: [June 2, 2022, 9:12pm UTC](https://discourse.julialang.org/t/using-optimization-parameters-in-modelingtoolkit/82099/4 "2022-06-02T21:12:48Z")
Thank you @YingboMa.
I fixed my code:
```julia
using ModelingToolkit, Plots, DifferentialEquations
using ModelingToolkitStandardLibrary.Electrical
@variables t
@named resistor = Resistor(R=1.)
@named capacitor = Capacitor(C=1.)
@named source = ConstantVoltage(V=1.)
@named ground = Ground()
rc_eqs = [
connect(source.p, resistor.p)
connect(resistor.n, capacitor.p)
connect(capacitor.n, source.n)
connect(capacitor.n, ground.g)
]
@named _rc_model = ODESystem(rc_eqs, t)
@named rc_model = compose(_rc_model,
[resistor, capacitor, source, ground])
sys = structural_simplify(rc_model)
u0 = [
capacitor.v => 0.0
]
params = [resistor.R => 1.0, capacitor.C => 1.0]
params_init = [1.0, 1.0]
tspan = (0., 10.)
prob = ODAEProblem(sys, u0, tspan, params)
sol = solve(prob, Tsit5(), p=params_init)
```
But the solution is wrong (constant zeros)
```julia
julia> sol
retcode: Success
Interpolation: specialized 4th order "free" interpolation
t: 9-element Vector{Float64}:
0.0
1.0e-6
1.1e-5
0.00011099999999999999
0.0011109999999999998
0.011110999999999996
0.11111099999999996
1.1111109999999995
10.0
u: 9-element Vector{Vector{Float64}}:
[0.0]
[1.3014e-320]
[1.4313e-319]
[1.44425e-318]
[1.4448273e-317]
[1.4377578e-316]
[1.36834641e-315]
[8.72596552e-315]
[-1.78158667517e-312]
```
---
### Author:  [@mzhenirovskyy](https://discourse.julialang.org/u/mzhenirovskyy)
#### Post date: [June 3, 2022, 7:29pm UTC](https://discourse.julialang.org/t/using-optimization-parameters-in-modelingtoolkit/82099/5 "2022-06-03T19:29:48Z")
@YingboMa Any idea why the solution is wrong? ^^^^
---
### Author:  [@YingboMa](https://discourse.julialang.org/u/YingboMa)
#### Post date: [June 6, 2022, 11:02pm UTC](https://discourse.julialang.org/t/using-optimization-parameters-in-modelingtoolkit/82099/6 "2022-06-06T23:02:40Z")
It should be
```julia
sol = solve(prob, Tsit5())
```
---
### Author:  [@mzhenirovskyy](https://discourse.julialang.org/u/mzhenirovskyy)
#### Post date: [June 7, 2022, 1:24am UTC](https://discourse.julialang.org/t/using-optimization-parameters-in-modelingtoolkit/82099/7 "2022-06-07T01:24:41Z")
@YingboMa
But I need to run optimization in respect of parameters `p`. It means I need to call the function `solve` with the different parameters many times. It seems `solve` supports such API.
For example in the docs, here you can see the call:
```julia
solve(prob_sde, SOSRI(), p=p,
sensealg = ForwardDiffSensitivity(), saveat = 0.1)
```
When I do
```julia
params = [resistor.R => 2.0, capacitor.C => 2.0]
prob = ODAEProblem(sys, u0, tspan, params)
```
```julia
julia> prob.p
3-element Vector{Float64}:
2.0
2.0
1.0
```
So instead of 2 parameters, we have 3, with **remake** we have 2 parameters.
```julia
prob1 = remake(prob; p=[2., 2.])
julia> prob1.p
2-element Vector{Float64}:
2.0
2.0
```
But the solution is still wrong:
```julia
sol = solve(prob1, Tsit5())
julia> sol
retcode: Success
Interpolation: specialized 4th order "free" interpolation
t: 7-element Vector{Float64}:
0.0
9.999999999999999e-5
0.0010999999999999998
0.011099999999999997
0.11109999999999996
1.1110999999999995
10.0
u: 7-element Vector{Vector{Float64}}:
[0.0]
[0.0]
[0.0]
[0.0]
[0.0]
[0.0]
[0.0]
julia>
```
---
### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [June 7, 2022, 10:26am UTC](https://discourse.julialang.org/t/using-optimization-parameters-in-modelingtoolkit/82099/8 "2022-06-07T10:26:39Z")
For now (until we fix it), you need to be more careful with `remake` on MTK generated problems. See the FAQ about this: [Frequently Asked Questions · ModelingToolkit.jl](https://mtk.sciml.ai/dev/basics/FAQ/#Transforming-value-maps-to-arrays)
---
### Author:  [@mzhenirovskyy](https://discourse.julialang.org/u/mzhenirovskyy)
#### Post date: [June 7, 2022, 8:28pm UTC](https://discourse.julialang.org/t/using-optimization-parameters-in-modelingtoolkit/82099/9 "2022-06-07T20:28:07Z")
@ChrisRackauckas @YingboMa
It seems my problem is in the set of parameters to set up.
If I use all 3 parameters to remake and solve, everything is OK.
But if I want to play only with 2 of them, it gives me the wrong solution.
So question is - what is the right way to play with only some subset of parameters and keep other defaults?
For example:
`sol = solve(prob1, Tsit5(), p=[1., 3., 7])`
gives the correct result
`sol = solve(prob1, Tsit5(), p=[1., 3.])`
gives the wrong result.
---
### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [June 7, 2022, 9:37pm UTC](https://discourse.julialang.org/t/using-optimization-parameters-in-modelingtoolkit/82099/10 "2022-06-07T21:37:37Z")
Can you open an issue? It’s weird it doesn’t just error with the wrong number of parameters so it would be good to dig into this one in more depth.
---
### Author:  [@mzhenirovskyy](https://discourse.julialang.org/u/mzhenirovskyy)
#### Post date: [June 8, 2022, 12:01am UTC](https://discourse.julialang.org/t/using-optimization-parameters-in-modelingtoolkit/82099/11 "2022-06-08T00:01:14Z")
@ChrisRackauckas
I’ve opened the issue.
[https://github.com/SciML/ModelingToolkit.jl/issues/1631](https://github.com/SciML/ModelingToolkit.jl/issues/1631)
But what is the right way of solving the system with the different parameters (a subset of parametrs) supposed to be?
---
### Author:  [@mzhenirovskyy](https://discourse.julialang.org/u/mzhenirovskyy)
#### Post date: [June 9, 2022, 7:02pm UTC](https://discourse.julialang.org/t/using-optimization-parameters-in-modelingtoolkit/82099/12 "2022-06-09T19:02:22Z")
@ChrisRackauckas @YingboMa
Sorry to bother you guys, but I just want to know the “official way” to run **Solver** with the subset of parameters, is it possible?
If it’s not supported, what is the right way to do optimization? Every time update only “optimization parameters” and keep the rest unmodified?
Thanks!
---
### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [June 11, 2022, 12:00pm UTC](https://discourse.julialang.org/t/using-optimization-parameters-in-modelingtoolkit/82099/13 "2022-06-11T12:00:02Z")
Your case has 3 parameters. Passing 2 just gives undefined behavior. We’re using this as a test case to add a size check in the MTK generated functions and in the `remake` phase to throw earlier. But in general, the Pair constructors are the only ones you really should be using here since otherwise you could be making assumptions about parameter ordering which may not be true post-simplification.