Model#

Bases: object

Represents a list of equations to be solved.

Parameters:

containerContainer: Container of the model.
namestr, optional: Name of the model. Name is autogenerated by default.
equationsSequence[Equation]: Sequence of Equation objects.
problemProblem or str, optional: ‘LP’, ‘NLP’, ‘QCP’, ‘DNLP’, ‘MIP’, ‘RMIP’, ‘MINLP’, ‘RMINLP’, ‘MIQCP’, ‘RMIQCP’, ‘MCP’, ‘CNS’, ‘MPEC’, ‘RMPEC’, ‘EMP’, or ‘MPSGE’, by default Problem.LP.
senseSense, optional: “MIN”, “MAX”, or “FEASIBILITY”.
objectiveVariable | Expression, optional: Objective variable to minimize or maximize or objective itself.
matchesdict[Equation, Variable]: Equation - Variable matches for MCP models.
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])

Attributes:

infeasibility_tolerance: This option sets the tolerance for marking an equation infeasible in the equation listing.

Methods

`compute_infeasibilities`()	Computes infeasabilities for all equations of the model
`freeze`(modifiables[, options])	Freezes all symbols except modifiable symbols.
`getDeclaration`()	Declaration of the Model in GAMS
`interrupt`()	Sends interrupt signal to the running job.
`solve`([solver, options, solver_options, ...])	Solves the model with given options.
`toGams`(path)	Generates GAMS model under path/<model_name>.gms
`unfreeze`()	Unfreezes the model

compute_infeasibilities() → dict[str, pd.DataFrame][source]#

Computes infeasabilities for all equations of the model

Returns:

dict[str, pd.DataFrame]: Dictionary of infeasibilities where equation names are keys and infeasibilities are values

Examples

>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["i1", "i2"])
>>> j = gp.Set(m, name="j", records=["j1", "j2", "j3"])
>>> a = gp.Parameter(m, name="a", domain=i, records=[("i1", 350), ("i2", 600)])
>>> b = gp.Parameter(m, name="b", domain=j, records=[("j1", 400), ("j2", 450), ("j3", 420)])
>>> x = gp.Variable(m, name="x", domain=[i,j], type="Positive")
>>> s = gp.Equation(m, name="s", domain=i)
>>> d = gp.Equation(m, name="d", domain=j)
>>> s[i] = gp.Sum(j, x[i, j]) <= a[i]
>>> d[j] = gp.Sum(i, x[i, j]) >= b[j]
>>> my_model = gp.Model(m, name="my_model", equations=m.getEquations(), problem="LP", sense="min", objective=gp.Sum((i, j), x[i, j]))
>>> summary = my_model.solve()
>>> infeasibilities = my_model.compute_infeasibilities()
>>> infeasibilities["s"].infeasibility.item()
320.0

property infeasibility_tolerance: float | None#

This option sets the tolerance for marking an equation infeasible in the equation listing. By default, 1.0e-13.

Returns:

float | None

interrupt() → None[source]#

Sends interrupt signal to the running job.

Raises:

ValidationError: If the job is not initialized

freeze(modifiables: list[Parameter | ImplicitParameter], options: Options | None = None) → None[source]#

Freezes all symbols except modifiable symbols.

Parameters:

modifiablesList[Parameter | ImplicitParameter]
freeze_optionsdict, optional

Examples

>>> import gamspy as gp
>>> m = gp.Container()
>>> a = gp.Parameter(m, name="a", records=10)
>>> x = gp.Variable(m, name="x")
>>> e = gp.Equation(m, name="e", definition= x <= a)
>>> my_model = gp.Model(m, name="my_model", equations=m.getEquations(), problem="LP", sense="max", objective=x)
>>> solved = my_model.solve()
>>> float(x.toValue())
10.0
>>> my_model.freeze(modifiables=[a])
>>> a.setRecords(35)
>>> solved = my_model.solve()
>>> float(x.toValue())
35.0

unfreeze() → None[source]#: Unfreezes the model

Solves the model with given options.

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
clientEngineClient, NeosClient, optional: EngineClient to communicate with GAMS Engine or NEOS Client to communicate with NEOS Server

Returns:

DataFrame, optional: Summary of the solve

Raises:

ValidationError: 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”

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], "max", v)
>>> solved = my_model.solve()

getDeclaration() → str[source]#

Declaration of the Model in GAMS

Returns:

str

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])
>>> my_model.getDeclaration()
'Model my_model / e /;'

toGams(path: str) → None[source]#

Generates GAMS model under path/<model_name>.gms

Parameters:

pathstr: Path to the directory which will contain the GAMS model.

class gamspy.ModelStatus(value)[source]#

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#

class gamspy.SolveStatus(value)[source]#

Bases: Enum

An enumeration for solve status types

NormalCompletion = 1#

IterationInterrupt = 2#

ResourceInterrupt = 3#

TerminatedBySolver = 4#

EvaluationInterrupt = 5#

CapabilityError = 6#

LicenseError = 7#

UserInterrupt = 8#

SetupError = 9#

SolverError = 10#

InternalError = 11#

Skipped = 12#

SystemError = 13#