Model

class gamspy.Model(container: Container, name: str, problem: str, equations: list[Equation] = [], sense: Literal['MIN', 'MAX', 'FEASIBILITY'] | None = None, objective: Variable | Expression | None = None, matches: dict | None = None, limited_variables: Iterable[Variable] | None = None)

Bases: object

Represents a list of equations to be solved.

Parameters:

containerContainer

The container that the model belongs to

namestr

Name of the model

equationsstr | Iterable

List of Equation objects or str. all as a string represents all the equations specified before the creation of this model

problemstr

Problem type (e.g. LP, NLP etc.)

sense“MIN”, “MAX”, or “FEASIBILITY”, optional

Minimize or maximize

objectiveVariable | Expression, optional

Objective variable to minimize or maximize or objective itself

limited_variablesIterable, optional

Allows limiting the domain of variables used in a model.

Examples

>>> import gamspy as gp
>>> m = gp.Container()
>>> v = gp.Variable(m, "v")
>>> e = gp.Equation(m, "e", definition= v == 5)
>>> my_model = gp.Model(m, "my_model", "LP", [e])

Methods

freeze(modifiables[, freeze_options])	Freezes all symbols except modifiable symbols.
getStatement()	Statement of the Model definition
interrupt()	Sends interrupt signal to the running job.
solve([solver, options, solver_options, ...])	Generates the gams string, writes it to a file and runs it
unfreeze()	Unfreezes all symbols

interrupt() → None

Sends interrupt signal to the running job.

Raises:

GamspyException

If the job is not initialized

freeze(modifiables: list[Parameter | ImplicitParameter], freeze_options: dict | None = None) → None

Freezes all symbols except modifiable symbols.

Parameters:

modifiablesList[Parameter | ImplicitParameter]

freeze_optionsdict, optional

unfreeze() → None

Unfreezes all symbols

solve(solver: str | None = None, options: Options | None = None, solver_options: dict | None = None, model_instance_options: dict | None = None, output: io.TextIOWrapper | None = None, backend: Literal['local', 'engine', 'neos'] = 'local', engine_config: EngineConfig | None = None, neos_client: NeosClient | None = None, create_log_file: bool = False) → pd.DataFrame | None

Generates the gams string, writes it to a file and runs it

Parameters:

solverstr, optional

Solver name

optionsOptions, optional

GAMS options

solver_optionsdict, optional

Solver options

model_instance_optionsoptional

Model instance options

outputTextIOWrapper, optional

Output redirection target

backendstr, optional

Backend to run on

engine_configEngineConfig, optional

GAMS Engine configuration

neos_clientNeosClient, optional

NEOS Client to communicate with NEOS Server

create_log_filebool

Allows creating a log file

Raises:

GamspyException

In case engine_config is not provided for engine backend or neos_client is not provided for neos backend.

ValueError

In case problem is not in possible problem types

ValueError

In case sense is different than “MIN” or “MAX”

getStatement() → str

Statement of the Model definition

Returns:

str

class gamspy.ModelStatus(value)

Bases: Enum

An enumeration for model status types

OptimalGlobal = 1

OptimalLocal = 2

Unbounded = 3

InfeasibleGlobal = 4

InfeasibleLocal = 5

InfeasibleIntermed = 6

Feasible = 7

Integer = 8

NonIntegerIntermed = 9

IntegerInfeasible = 10

LicenseError = 11

ErrorUnknown = 12

ErrorNoSolution = 13

NoSolutionReturned = 14

SolvedUnique = 15

Solved = 16

SolvedSingular = 17

UnboundedNoSolution = 18

InfeasibleNoSolution = 19