https://github.com/yuricst/joptimise
Julia wrapper to ipopt and snopt
https://github.com/yuricst/joptimise
Last synced: 3 months ago
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Julia wrapper to ipopt and snopt
- Host: GitHub
- URL: https://github.com/yuricst/joptimise
- Owner: Yuricst
- License: mit
- Created: 2021-06-07T16:06:26.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2023-01-25T02:49:35.000Z (over 3 years ago)
- Last Synced: 2025-10-28T17:44:24.051Z (7 months ago)
- Language: Julia
- Size: 135 KB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# joptimise
`joptimise.jl` (Fr: j'optimise) is a Julia wrapper to [Ipopt](https://coin-or.github.io/Ipopt/) and [SNOPT](https://ccom.ucsd.edu/~optimizers/docs/snopt/) for solving nonlinear programming problems with gradient methods.
Parts borrowed from [Snow.jl](https://github.com/byuflowlab/SNOW.jl) and [SNOPT7.jl](https://github.com/snopt/SNOPT7.jl).
## Dependencies
`Ipopt`, `FiniteDiff`, `ForwardDiff`, `ReverseDiff`, `SparseArrays`
## Environment setup
For using `SNOPT`, users must also have an active license.
### Windows (Windows Subsystem for Linux)
Usage on native Windows is discouraged; setting up a Julia environment on WSL seems to work well with SNOPT. Set as environment variables in `~/.bashrc` (working on WSL):
```bash
export SNOPT_LICENSE="$HOME/path-to/snopt7.lic"
export SNOPT_SO="$HOME/path-to/libsnopt7/libsnopt7.so"
```
Then, internally, the SNOPT library is called via
```julia
const snoptlib = ENV["SNOPT_SO"]
```
## Installation
```julia-repl
(@v1.6) pkg> dev https://github.com/Yuricst/joptimise.git
```
## Usage
Import module, define objective function which mutates the constraint value `g` and returns the objective value:
```julia
using joptimise
function rosenbrock!(g, x)
# compute objective
f = (1 - x[1])^2 + 100*(x[2] - x[1]^2)^2
# constraint
g[1] = x[1]^2 + x[2]^2 - 1.0
return f
end
# initial guess
x0 = [4.0; 4.0]
# bounds on variables
lx = [-5.0; -5.0]
ux = [5.0; 5.0]
# bounds on constraints
lg = [0.0]
ug = [0.0]
# number of constraints
ng = 1
```
then call SNOPT
```julia
sn_options = Dict(
"Major feasibility tolerance" => 1.e-6,
"Major optimality tolerance" => 1.e-6,
"Minor feasibility tolerance" => 1.e-6,
"Major iterations limit" => 1000,
"Major print level" => 1,
)
xopt, fopt, info = minimize(rosenbrock!, x0, ng; lx=lx, ux=ux, lg=lg, ug=ug, solver="snopt", options=sn_options)
```
or IPOPT
```julia
ip_options = Dict(
"max_iter" => 2500,
"tol" => 1e-6,
"print_level" => 5,
)
xopt, fopt, info = minimize(rosenbrock!, x0, ng; lx=lx, ux=ux, lg=lg, ug=ug, solver="ipopt", options=ip_options)
```
### Specifying derivative method
By default, `minimize()` will compute derivatives of the objective and constraints using forward finite-difference from `ForwardDiff`. This may be altered to central-difference, forward-mode or reverse-mode AD from `ForwardDiff` or `ReverseDiff`. This is passed as the kwargs `derivatives`; the possible options are `ForwardFD()`, `CentralFD()`, `ForwardAD()`, or `ReverseAD()`. For example, if using forward-mode AD,
```julia
xopt, fopt, info = minimize(rosenbrock!, x0, ng; lx=lx, ux=ux, lg=lg, ug=ug, solver="snopt", options=sn_options, derivatives=ForwardAD())
```
## Todo
- [ ] user-specified derivative
- [ ] optional output file using SNOPT