# GAMS.jl

GAMS.jl provides a MathOptInterface Optimizer to solve JuMP models using GAMS.

GAMS comes with dozens of supported solvers. Among them are: ALPHAECP, ANTIGONE, BARON, CBC, CONOPT, CPLEX, DICOPT, GUROBI, IPOPT, KNITRO, LINDO, LINDOGLOBAL, MINOS, MOSEK, NLPEC, PATH, QUADMINOS, SBB, SHOT, SCIP, SNOPT, SOPLEX, XPRESS. Find a complete list here.

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

- Download GAMS and obtain a GAMS license. Please note that GAMS also offers a free community license.
- (optional) Add the GAMS system directory to the
`PATH`

variable in order to find GAMS automatically. - Install GAMS.jl using the Julia package manager:
using Pkg Pkg.add("GAMS")

## Usage

Using GAMS as optimizer for your JuMP model:

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):

set_optimizer_attribute(model, "SysDir", "<gams_system_dir>")
set_optimizer_attribute(model, GAMS.SysDir(), "<gams_system_dir>")

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

ws = GAMS.GAMSWorkspace()
ws = GAMS.GAMSWorkspace("<gams_system_dir>")
ws = GAMS.GAMSWorkspace("<gams_system_dir>", "<gams_working_dir>")

and then pass it to your models:

model = Model(() -> GAMS.Optimizer(ws))

### GAMS Options

GAMS command line options can be specified by

set_optimizer_attribute(model, "<option>", "<solver_name>")
set_optimizer_attribute(model, GAMS.<option>(), "<solver_name>")

where `<option>`

is either
HoldFixed,
IterLim,
License,
LogOption,
NodLim,
OptCA,
OptCR,
ResLim,
Solver,
Threads,
Trace,
TraceOpt as well as
LP,
MIP,
RMIP,
NLP,
DNLP,
CNS,
MINLP,
RMINLP,
QCP,
MIQCP,
RMIQCP,
MCP or
MPEC.
Note that `GAMS.ResLim()`

is equivalent to `MOI.TimeLimitSec()`

and
`GAMS.Threads()`

to `MOI.NumberOfThreads()`

. Options
LimCol,
LimRow,
SolPrint and
SolveLink
cannot be changed and are set to `0`

, `0`

, `0`

and `5`

, respectively.

#### Model Type

GAMS.jl will automatically choose a GAMS model type for you. Choosing a different model type:

set_optimizer_attribute(model, GAMS.ModelType(), "<model_type>")

### GAMS Solver Options

Specifying GAMS solver options:

set_optimizer_attribute(model, "<solver_option_name>", <option_value>)

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:

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.