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https://github.com/GAMS-dev/gams.jl
A MathOptInterface Optimizer to solve JuMP models using GAMS
https://github.com/GAMS-dev/gams.jl
gams julia jump modeling-language optimization
Last synced: about 2 months ago
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A MathOptInterface Optimizer to solve JuMP models using GAMS
- Host: GitHub
- URL: https://github.com/GAMS-dev/gams.jl
- Owner: GAMS-dev
- License: mit
- Created: 2020-05-14T12:31:41.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-03-28T09:20:53.000Z (6 months ago)
- Last Synced: 2024-07-28T12:07:31.335Z (about 2 months ago)
- Topics: gams, julia, jump, modeling-language, optimization
- Language: Julia
- Homepage:
- Size: 183 KB
- Stars: 34
- Watchers: 8
- Forks: 3
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG
- License: LICENSE
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- awesome-sciml - GAMS-dev/gams.jl: A MathOptInterface Optimizer to solve JuMP models using GAMS
README
# GAMS.jl
GAMS.jl provides a
[MathOptInterface](https://github.com/JuliaOpt/MathOptInterface.jl) Optimizer to
solve [JuMP](https://github.com/JuliaOpt/JuMP.jl) models using
[GAMS](https://www.gams.com/).
GAMS comes with dozens of supported solvers. Among them are:
[ALPHAECP](https://www.gams.com/latest/docs/S_ALPHAECP.html),
[ANTIGONE](https://www.gams.com/latest/docs/S_ANTIGONE.html),
[BARON](https://www.gams.com/latest/docs/S_BARON.html),
[CBC](https://www.gams.com/latest/docs/S_CBC.html),
[CONOPT](https://www.gams.com/latest/docs/S_CONOPT.html),
[CPLEX](https://www.gams.com/latest/docs/S_CPLEX.html),
[DICOPT](https://www.gams.com/latest/docs/S_DICOPT.html),
[GUROBI](https://www.gams.com/latest/docs/S_GUROBI.html),
[IPOPT](https://www.gams.com/latest/docs/S_IPOPT.html),
[KNITRO](https://www.gams.com/latest/docs/S_KNITRO.html),
[LINDO](https://www.gams.com/latest/docs/S_LINDO.html),
[LINDOGLOBAL](https://www.gams.com/latest/docs/S_LINDO.html),
[MINOS](https://www.gams.com/latest/docs/S_MINOS.html),
[MOSEK](https://www.gams.com/latest/docs/S_MOSEK.html),
[NLPEC](https://www.gams.com/latest/docs/S_NLPEC.html),
[PATH](https://www.gams.com/latest/docs/S_PATH.html),
[QUADMINOS](https://www.gams.com/latest/docs/S_MINOS.html),
[SBB](https://www.gams.com/latest/docs/S_SBB.html),
[SHOT](https://www.gams.com/latest/docs/S_SHOT.html),
[SCIP](https://www.gams.com/latest/docs/S_SCIP.html),
[SNOPT](https://www.gams.com/latest/docs/S_SNOPT.html),
[SOPLEX](https://www.gams.com/latest/docs/S_SOPLEX.html),
[XPRESS](https://www.gams.com/latest/docs/S_XPRESS.html).
Find a complete list [here](https://www.gams.com/latest/docs/S_MAIN.html).
GAMS.jl supports the following JuMP features:
- linear, quadratic and nonlinear (convex and non-convex) objective and constraints
- continuous, binary, integer, semi-continuous and semi-integer variables
- SOS1 and SOS2 sets
- complementarity constraints
## Installation
1. [Download GAMS](https://www.gams.com/download/) and obtain a
GAMS license. Please note that GAMS also offers a [free community
license](https://www.gams.com/latest/docs/UG_License.html#GAMS_Community_Licenses).
2. (optional) Add the GAMS system directory to the `PATH` variable in order to
find GAMS automatically.
3. Install GAMS.jl using the Julia package manager:
```julia
using Pkg
Pkg.add("GAMS")
```
## Usage
Using GAMS as optimizer for your JuMP model:
```julia
using GAMS, JuMP
model = Model(GAMS.Optimizer)
```
### GAMS System
If the GAMS system directory has been added to the `PATH` variable (you can
check this with `print(ENV["PATH"])`), GAMS.jl will find it automatically.
Otherwise, or if you like to switch between systems, the system directory can be
specified by (one of the following):
```julia
set_optimizer_attribute(model, "SysDir", "")
set_optimizer_attribute(model, GAMS.SysDir(), "")
```
Analogously, you can specify a working directory with `"WorkDir"` or
`GAMS.WorkDir()`. If no working directory has been set, GAMS.jl will create a
temporary one.
If you want to use the same GAMS workspace (same system and working directory)
for multiple models, you can create a `GAMSWorkspace` first with either of the
following
```julia
ws = GAMS.GAMSWorkspace()
ws = GAMS.GAMSWorkspace("")
ws = GAMS.GAMSWorkspace("", "")
```
and then pass it to your models:
```julia
model = Model(() -> GAMS.Optimizer(ws))
```
### GAMS Options
[GAMS command line options](https://www.gams.com/latest/docs/UG_GamsCall.html#UG_GamsCall_ListOfCommandLineParameters) can be specified by
```julia
set_optimizer_attribute(model, "", "")
set_optimizer_attribute(model, GAMS.(), "")
```
where `` is either
[HoldFixed](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOholdfixed),
[IterLim](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOiterlim),
[License](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOlicense),
[LogOption](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOlogoption),
[NodLim](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOnodlim),
[OptCA](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOoptca),
[OptCR](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOoptcr),
[ResLim](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOreslim),
[Solver](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOsolver),
[Threads](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOthreads),
[Trace](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOtrace),
[TraceOpt](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOtraceopt) as well as
[LP](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOlp),
[MIP](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOmip),
[RMIP](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOrmip),
[NLP](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOnlp),
[DNLP](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOdnlp),
[CNS](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOcns),
[MINLP](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOminlp),
[RMINLP](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOrminlp),
[QCP](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOqcp),
[MIQCP](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOmiqcp),
[RMIQCP](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOrmiqcp),
[MCP](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOmcp) or
[MPEC](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOmpec).
Note that `GAMS.ResLim()` is equivalent to `MOI.TimeLimitSec()` and
`GAMS.Threads()` to `MOI.NumberOfThreads()`. Options
[LimCol](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOlimcol),
[LimRow](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOlimrow),
[SolPrint](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOsolprint) and
[SolveLink](https://www.gams.com/latest/docs/UG_GamsCall.html#GAMSAOsolvelink)
cannot be changed and are set to `0`, `0`, `0` and `5`, respectively.
#### Model Type
GAMS.jl will automatically choose a [GAMS model
type](https://www.gams.com/latest/docs/UG_ModelSolve.html#UG_ModelSolve_ModelClassificationOfModels)
for you. Choosing a different model type:
```julia
set_optimizer_attribute(model, GAMS.ModelType(), "")
```
### GAMS Solver Options
Specifying GAMS solver options:
```julia
set_optimizer_attribute(model, "", )
```
Note that passing a solver option is only valid when explicitly choosing a GAMS
solver and not using the default.
### GAMS Names vs. JuMP Names
GAMS uses generated variable and constraint names although it is possible to
pass the JuMP names to the GAMS optimizer, because GAMS is more restrictive
when it comes to variable and constraint naming. Use the attributes
`GeneratedVariableName`, `GeneratedConstraintName`, `OriginalVariableName`,
`OriginalConstraintName` to query a GAMS symbol name from a JuMP symbol and vice
versa. This can help for debugging, for example in case of GAMS compilation errors.
For example:
```julia
using GAMS
model = direct_model(GAMS.Optimizer())
@variable(model, x[1:2,1:3] >= 0)
@constraint(model, c[i = 1:2], sum(x[i,j] for j = 1:3) <= 10)
MOI.get(model, GAMS.GeneratedVariableName(), x[2,2]) # returns x4
MOI.get(model, GAMS.OriginalVariableName("x6")) # returns x[2,3]
MOI.get(model, GAMS.OriginalVariableName("x10")) # returns nothing
MOI.get(model, GAMS.GeneratedConstraintName(), c[2]) # returns eq2
MOI.get(model, GAMS.OriginalConstraintName("eq1")) # returns c[1]
MOI.get(model, GAMS.OriginalConstraintName("eq10")) # returns nothing
```
Note that JuMP direct-mode is used.