Equation#

Bases: Equation, Symbol

Represents an Equation symbol in GAMS. https://www.gams.com/latest/docs/UG_Equations.html

Parameters:

containerContainer: Container of the variable.
namestr, optional: Name of the equation. Name is autogenerated by default.
typestr: Type of the equation. “regular” by default.
domainlist[Set | Alias | str] | Set | Alias | str, optional: Domain of the variable.
definition: Expression, optional: Definition of the equation.
recordsAny, optional: Records of the equation.
domain_forwardingbool, optional: Whether the equation forwards the domain. See: https://gams.com/latest/docs/UG_SetDefinition.html#UG_SetDefinition_ImplicitSetDefinition
descriptionstr, optional: Description of the equation.
uels_on_axes: bool: Assume that symbol domain information is contained in the axes of the given records.
definition_domain: list, optional: Definiton domain of the equation.
is_miro_outputbool: Whether the symbol is a GAMS MIRO output symbol. See: https://gams.com/miro/tutorial.html

Attributes:

container: Container of the symbol
default_records: Default records of an equation
description: Description of the symbol
dimension: The dimension of symbol
domain: List of domains given either as string (* for universe set) or as reference to the Set/Alias object
domain_forwarding: A boolean indicating whether domain forwarding is enabled
domain_labels: The column headings for the records DataFrame
domain_names: String version of domain names
domain_type: State of the domain links
infeas: Infeasability
is_scalar: Returns True if the len(self.domain) = 0
l: Level
lo: Lower bound
m: Marginal
modified: Flag that identifies if the symbol has been modified
name: Name of symbol
number_records: Number of records
range: Range
records: Records of the Equation
scale: Scale
shape: Returns a tuple describing the array dimensions if records were converted with .toDense()
slack: Slack
slacklo: Slack lower bound
slackup: Slack upper bound
stage: Stage
summary: Summary of the symbol
type: The type of equation; 3.
up: Upper bound

Methods

`computeInfeasibilities`()	Computes infeasabilities of the equation
`countEps`([columns])	Counts total number of SpecialValues.EPS across columns
`countNA`([columns])	Counts total number of SpecialValues.NA across columns
`countNegInf`([columns])	Counts total number of SpecialValues.NegInf across columns
`countPosInf`([columns])	Counts total number of SpecialValues.PosInf across columns
`countUndef`([columns])	Counts total number of SpecialValues.Undef across columns
`dropDefaults`()	Drop records that are set to GAMS default records (check .default_records property for values)
`dropEps`()	Drop records from the symbol that are GAMS EPS (zero 0.0 records will be retained)
`dropMissing`()	Drop records from the symbol that are NaN (includes both NA and Undef special values)
`dropNA`()	Drop records from the symbol that are GAMS NA
`dropUndef`()	Drop records from the symbol that are GAMS Undef
`equals`(other[, columns, check_uels, ...])	Used to compare the symbol to another symbol
`findEps`([column])	Find positions of SpecialValues.EPS in value column
`findNA`([column])	Find positions of SpecialValues.NA in value column
`findNegInf`([column])	Find positions of SpecialValues.NegInf in value column
`findPosInf`([column])	Find positions of SpecialValues.PosInf in value column
`findSpecialValues`(values[, column])	Find positions of specified values in records columns
`findUndef`([column])	Find positions of SpecialValues.Undef in value column
`gamsRepr`()	Representation of this Equation in GAMS language.
`generateRecords`([density, func, seed])	Convenience method to set standard pandas.DataFrame formatted records given domain set information.
`getDeclaration`()	Declaration of the Equation in GAMS
`getDefinition`()	Definition of the Equation in GAMS
`getEquationListing`([n, filters, ...])	Returns the generated equations.
`getMaxAbsValue`([columns])	Get the maximum absolute value across chosen columns
`getMaxValue`([columns])	Get the maximum value across chosen columns
`getMeanValue`([columns])	Get the mean value across chosen columns
`getMinValue`([columns])	Get the minimum value across chosen columns
`getSparsity`()	Get the sparsity of the symbol w.r.t the cardinality
`isValid`([verbose, force])	Checks if the symbol is in a valid format
`pivot`([index, columns, value, fill_value])	Convenience function to pivot records into a new shape (only symbols with >1D can be pivoted)
`setRecords`(records[, uels_on_axes])	Main convenience method to set standard pandas.DataFrame formatted records.
`toDense`([column])	Convert column to a dense numpy.array format
`toDict`([columns, orient])	Convenience method to return symbol records as a Python dictionary
`toList`([columns])	Convenience method to return symbol records as a Python list
`toSparseCoo`([column])	Convert column to a sparse COOrdinate numpy.array format
`toValue`([column])	Convenience method to return symbol records as a Python float.
`whereMax`([column])	Find the domain entry of records with a maximum value (return first instance only)
`whereMaxAbs`([column])	Find the domain entry of records with a maximum absolute value (return first instance only)
`whereMin`([column])	Find the domain entry of records with a minimum value (return first instance only)

Examples

>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, "i", records=['i1','i2'])
>>> a = gp.Parameter(m, "a", [i], records=[['i1',1],['i2',2]])
>>> v = gp.Variable(m, "v", domain=[i])
>>> e = gp.Equation(m, "e", domain=[i])
>>> e[i] = a[i] <= v[i]

property description#: Description of the symbol

property modified#: Flag that identifies if the symbol has been modified

property l#

Level

Returns:

ImplicitParameter

Examples

>>> from gamspy import Container, Parameter, Variable, Equation, Model
>>> m = Container()
>>> x1 = Variable(m, "x1", type="Positive")
>>> x2 = Variable(m, "x2", type="Positive")
>>> z = Variable(m, "z")
>>> eq = Equation(m, "eq")
>>> eq[...] = 2*x1 + 3*x2 <= 10
>>> solved_model = Model(m, "my_model", equations=[eq], objective=10*x1 + 6*x2, sense="MAX").solve()
>>> repr = Parameter(m, "repr")
>>> repr[...] = eq.l
>>> repr.toValue()
np.float64(10.0)

property m#

Marginal

Returns:

ImplicitParameter

Examples

>>> from gamspy import Container, Parameter, Variable, Equation, Model
>>> m = Container()
>>> x1 = Variable(m, "x1", type="Positive")
>>> x2 = Variable(m, "x2", type="Positive")
>>> z = Variable(m, "z")
>>> eq = Equation(m, "eq")
>>> eq[...] = 2*x1 + 3*x2 <= 10
>>> solved_model = Model(m, "my_model", equations=[eq], objective=10*x1 + 6*x2, sense="MAX").solve()
>>> repr = Parameter(m, "repr")
>>> repr[...] = eq.m
>>> repr.toValue()
np.float64(5.0)

property lo#

Lower bound

Returns:

ImplicitParameter

Examples

>>> from gamspy import Container, Parameter, Variable, Equation, Model
>>> m = Container()
>>> x1 = Variable(m, "x1", type="Positive")
>>> x2 = Variable(m, "x2", type="Positive")
>>> z = Variable(m, "z")
>>> eq = Equation(m, "eq")
>>> eq[...] = 2*x1 + 3*x2 <= 10
>>> solved_model = Model(m, "my_model", equations=[eq], objective=10*x1 + 6*x2, sense="MAX").solve()
>>> repr = Parameter(m, "repr")
>>> repr[...] = eq.lo
>>> repr.toValue()
np.float64(-inf)

property up#

Upper bound

Returns:

ImplicitParameter

Examples

>>> from gamspy import Container, Parameter, Variable, Equation, Model
>>> m = Container()
>>> x1 = Variable(m, "x1", type="Positive")
>>> x2 = Variable(m, "x2", type="Positive")
>>> z = Variable(m, "z")
>>> eq = Equation(m, "eq")
>>> eq[...] = 2*x1 + 3*x2 <= 10
>>> solved_model = Model(m, "my_model", equations=[eq], objective=10*x1 + 6*x2, sense="MAX").solve()
>>> repr = Parameter(m, "repr")
>>> repr[...] = eq.up
>>> repr.toValue()
np.float64(10.0)

property scale#

Scale

Returns:

ImplicitParameter

Examples

>>> from gamspy import Container, Parameter, Variable, Equation, Model
>>> m = Container()
>>> x1 = Variable(m, "x1", type="Positive")
>>> x2 = Variable(m, "x2", type="Positive")
>>> z = Variable(m, "z")
>>> eq = Equation(m, "eq")
>>> eq[...] = 2*x1 + 3*x2 <= 10
>>> solved_model = Model(m, "my_model", equations=[eq], objective=10*x1 + 6*x2, sense="MAX").solve()
>>> repr = Parameter(m, "repr")
>>> repr[...] = eq.scale
>>> repr.toValue()
np.float64(1.0)

property stage#

Stage

Returns:

ImplicitParameter

Examples

>>> from gamspy import Container, Parameter, Variable, Equation, Model
>>> m = Container()
>>> x1 = Variable(m, "x1", type="Positive")
>>> x2 = Variable(m, "x2", type="Positive")
>>> z = Variable(m, "z")
>>> eq = Equation(m, "eq")
>>> eq[...] = 2*x1 + 3*x2 <= 10
>>> solved_model = Model(m, "my_model", equations=[eq], objective=10*x1 + 6*x2, sense="MAX").solve()
>>> repr = Parameter(m, "repr")
>>> repr[...] = eq.stage
>>> repr.toValue()
np.float64(1.0)

property range#

Range

Returns:

ImplicitParameter

Examples

>>> from gamspy import Container, Parameter, Variable, Equation, Model
>>> m = Container()
>>> x1 = Variable(m, "x1", type="Positive")
>>> x2 = Variable(m, "x2", type="Positive")
>>> z = Variable(m, "z")
>>> eq = Equation(m, "eq")
>>> eq[...] = 2*x1 + 3*x2 <= 10
>>> solved_model = Model(m, "my_model", equations=[eq], objective=10*x1 + 6*x2, sense="MAX").solve()
>>> repr = Parameter(m, "repr")
>>> repr[...] = eq.range
>>> repr.toValue()
np.float64(inf)

property slacklo#

Slack lower bound

Returns:

ImplicitParameter

Examples

>>> from gamspy import Container, Parameter, Variable, Equation, Model
>>> m = Container()
>>> x1 = Variable(m, "x1", type="Positive")
>>> x2 = Variable(m, "x2", type="Positive")
>>> z = Variable(m, "z")
>>> eq = Equation(m, "eq")
>>> eq[...] = 2*x1 + 3*x2 <= 10
>>> solved_model = Model(m, "my_model", equations=[eq], objective=10*x1 + 6*x2, sense="MAX").solve()
>>> repr = Parameter(m, "repr")
>>> repr[...] = eq.slacklo
>>> repr.toValue()
np.float64(inf)

property slackup#

Slack upper bound

Returns:

ImplicitParameter

Examples

>>> from gamspy import Container, Parameter, Variable, Equation, Model
>>> m = Container()
>>> x1 = Variable(m, "x1", type="Positive")
>>> x2 = Variable(m, "x2", type="Positive")
>>> z = Variable(m, "z")
>>> eq = Equation(m, "eq")
>>> eq[...] = 2*x1 + 3*x2 <= 10
>>> solved_model = Model(m, "my_model", equations=[eq], objective=10*x1 + 6*x2, sense="MAX").solve()
>>> repr = Parameter(m, "repr")
>>> repr[...] = eq.slackup
>>> repr.toValue()
np.float64(0.0)

property slack#

Slack

Returns:

ImplicitParameter

Examples

>>> from gamspy import Container, Parameter, Variable, Equation, Model
>>> m = Container()
>>> x1 = Variable(m, "x1", type="Positive")
>>> x2 = Variable(m, "x2", type="Positive")
>>> z = Variable(m, "z")
>>> eq = Equation(m, "eq")
>>> eq[...] = 2*x1 + 3*x2 <= 10
>>> solved_model = Model(m, "my_model", equations=[eq], objective=10*x1 + 6*x2, sense="MAX").solve()
>>> repr = Parameter(m, "repr")
>>> repr[...] = eq.slack
>>> repr.toValue()
np.float64(0.0)

property infeas#

Infeasability

Returns:

ImplicitParameter

Examples

>>> from gamspy import Container, Parameter, Variable, Equation, Model
>>> m = Container()
>>> x1 = Variable(m, "x1", type="Positive")
>>> x2 = Variable(m, "x2", type="Positive")
>>> z = Variable(m, "z")
>>> eq = Equation(m, "eq")
>>> eq[...] = 2*x1 + 3*x2 <= 10
>>> solved_model = Model(m, "my_model", equations=[eq], objective=10*x1 + 6*x2, sense="MAX").solve()
>>> repr = Parameter(m, "repr")
>>> repr[...] = eq.infeas
>>> repr.toValue()
np.float64(0.0)

computeInfeasibilities() → DataFrame[source]#

Computes infeasabilities of the equation

Returns:

pd.DataFrame

Examples

>>> import gamspy as gp
>>> m = gp.Container()
>>> e = gp.Equation(m, "e")
>>> e.l[...] = -10
>>> e.lo[...] = 5
>>> e.computeInfeasibilities().values.tolist()
[[-10.0, 0.0, 5.0, 0.0, 1.0, 10.0]]

getEquationListing(n: int | None = None, filters: list[list[str]] | None = None, infeasibility_threshold: float | None = None) → list[str][source]#

Returns the generated equations.

Parameters:

nint, optional: Number of equations to be returned.
filterslist[list[str]], optional: Filters to be used.
infeasibility_threshold: float, optional: Filters out equations with infeasibilities that are above this value.

Returns:

list[str]

Raises:

ValidationError: In case the model is not solved yet with equation_listing_limit option.
ValidationError: In case the length of the filters is different than the dimension of the equation.

property container#: Container of the symbol

countEps(columns: str | List[str] | None = None) → int#

Counts total number of SpecialValues.EPS across columns

Parameters:

columnsstr | List[str], optional: Columns to count special values in, by default None

Returns:

Total number of SpecialValues.EPS across columns

countNA(columns: str | List[str] | None = None) → int#

Counts total number of SpecialValues.NA across columns

Parameters:

columnsstr | List[str], optional: Columns to count special values in, by default None

Returns:

Total number of SpecialValues.NA across columns

countNegInf(columns: str | List[str] | None = None) → int#

Counts total number of SpecialValues.NegInf across columns

Parameters:

columnsstr | List[str], optional: Columns to count special values in, by default None

Returns:

Total number of SpecialValues.NegInf across columns

countPosInf(columns: str | List[str] | None = None) → int#

Counts total number of SpecialValues.PosInf across columns

Parameters:

columnsstr | List[str], optional: Columns to count special values in, by default None

Returns:

Total number of SpecialValues.PosInf across columns

countUndef(columns: str | List[str] | None = None) → int#

Counts total number of SpecialValues.Undef across columns

Parameters:

columnsstr | List[str], optional: Columns to count special values in, by default None

Returns:

Total number of SpecialValues.Undef across columns

property default_records#: Default records of an equation

property dimension#: The dimension of symbol

property domain#: List of domains given either as string (* for universe set) or as reference to the Set/Alias object

property domain_forwarding#: A boolean indicating whether domain forwarding is enabled

property domain_labels#: The column headings for the records DataFrame

property domain_names#: String version of domain names

property domain_type#: State of the domain links

dropDefaults() → None#: Drop records that are set to GAMS default records (check .default_records property for values)

dropEps() → None#: Drop records from the symbol that are GAMS EPS (zero 0.0 records will be retained)

dropMissing() → None#: Drop records from the symbol that are NaN (includes both NA and Undef special values)

dropNA() → None#: Drop records from the symbol that are GAMS NA

dropUndef() → None#: Drop records from the symbol that are GAMS Undef

equals(other: Variable, columns: str = None, check_uels: bool = True, check_meta_data: bool = True, rtol: int | float | None = None, atol: int | float | None = None, verbose: bool = False) → bool#

Used to compare the symbol to another symbol

Parameters:

otherVariable: _description_
columnsstr, optional: allows the user to numerically compare only specified variable attributes, by default None; compare all
check_uelsbool, optional: If True, check both used and unused UELs and confirm same order, otherwise only check used UELs in data and do not check UEL order. by default True
check_meta_databool, optional: If True, check that symbol name and description are the same, otherwise skip. by default True
rtolint | float, optional: relative tolerance, by default None
atolint | float, optional: absolute tolerance, by default None
verbosebool, optional: If True, will return an exception from the asserter describing the nature of the difference. by default False

Returns:

bool: True if symbols are equal, False otherwise

findEps(column: str | None = None) → DataFrame#

Find positions of SpecialValues.EPS in value column

Parameters:

columnstr, optional: Column to find the special values in, by default None

Returns:

pd.DataFrame: Dataframe containing special values

findNA(column: str | None = None) → DataFrame#

Find positions of SpecialValues.NA in value column

Parameters:

columnstr, optional: Column to find the special values in, by default None

Returns:

pd.DataFrame: Dataframe containing special values

findNegInf(column: str | None = None) → DataFrame#

Find positions of SpecialValues.NegInf in value column

Parameters:

columnstr, optional: Column to find the special values in, by default None

Returns:

pd.DataFrame: Dataframe containing special values

findPosInf(column: str | None = None) → DataFrame#

Find positions of SpecialValues.PosInf in value column

Parameters:

columnstr, optional: Column to find the special values in, by default None

Returns:

pd.DataFrame: Dataframe containing special values

findSpecialValues(values: float | List[float], column: str | None = None) → DataFrame#

Find positions of specified values in records columns

Parameters:

valuesfloat | List[float]: Values to look for
columnstr, optional: Column to find the special values in, by default None

Returns:

pd.DataFrame: Dataframe containing special values

findUndef(column: str | None = None) → DataFrame#

Find positions of SpecialValues.Undef in value column

Parameters:

columnstr, optional: Column to find the special values in, by default None

Returns:

pd.DataFrame: Dataframe containing special values

Convenience method to set standard pandas.DataFrame formatted records given domain set information. Will generate records with the Cartesian product of all domain sets.

Parameters:

densityint | float | list, optional: Takes any value on the interval [0,1]. If density is <1 then randomly selected records will be removed. density will accept a list of length dimension – allows users to specify a density per symbol dimension, by default None
funcCallable, optional: Functions to generate the records, by default None; numpy.random.uniform(0,1)
seedint, optional: Random number state can be set with seed argument, by default None

getMaxAbsValue(columns: str | List[str] | None = None) → float#

Get the maximum absolute value across chosen columns

Parameters:

columnsstr | List[str], optional: Columns to find maximum absolute values in, by default None

Returns:

float: Maximum absolute value

getMaxValue(columns: str | List[str] | None = None) → float#

Get the maximum value across chosen columns

Parameters:

columnsstr | List[str], optional: Columns to find maximum values in, by default None

Returns:

float: Maximum value

getMeanValue(columns: str | List[str] | None = None) → float#

Get the mean value across chosen columns

Parameters:

columnsstr | List[str], optional: Columns to find mean values in, by default None

Returns:

float: Mean value

getMinValue(columns: str | List[str] | None = None) → float#

Get the minimum value across chosen columns

Parameters:

columnsstr | List[str], optional: Columns to find minimum values in, by default None

Returns:

float: Minimum value

getSparsity() → float#

Get the sparsity of the symbol w.r.t the cardinality

Returns:

float: Sparsity of the symbol w.r.t the cardinality

isValid(verbose: bool = False, force: bool = False) → bool#

Checks if the symbol is in a valid format

Parameters:

verbosebool, optional: Throw exceptions if verbose=True, by default False
forcebool, optional: Recheck a symbol if force=True, by default False

Returns:

bool: True if a symbol is in valid format, False otherwise (throws exceptions if verbose=True)

property is_scalar: bool#

Returns True if the len(self.domain) = 0

Returns:

bool: True if the len(self.domain) = 0

property name#: Name of symbol

property number_records#: Number of records

Convenience function to pivot records into a new shape (only symbols with >1D can be pivoted)

Parameters:

indexstr | list, optional: If index is None then it is set to dimensions [0..dimension-1], by default None
columnsstr | list, optional: If columns is None then it is set to the last dimension, by default None
valuestr, optional: If value is None then the level values will be pivoted, by default None
fill_valueint | float | str, optional: Missing values in the pivot will take the value provided by fill_value, by default None

Returns:

DataFrame: Pivoted records dataframe

property shape: tuple#

Returns a tuple describing the array dimensions if records were converted with .toDense()

Returns:

tuple: A tuple describing the records dimensions

property summary#: Summary of the symbol

toDense(column: str = 'level') → ndarray | None#

Convert column to a dense numpy.array format

Parameters:

columnstr, optional: The column to convert, by default “level”

Returns:

np.ndarray, optional: A column to a dense numpy.array format

toDict(columns: str | None = None, orient: str | None = None) → dict#

Convenience method to return symbol records as a Python dictionary

Parameters:

columnsstr, optional: Controls which attributes to include in the dict, by default None
orientstr, optional: Orient can take values natural or columns and will control the shape of the dict. Must use orient=”columns” if attempting to set symbol records with setRecords, by default None

Returns:

dict: Records as a Python dictionary

toList(columns: str | None = None) → list#

Convenience method to return symbol records as a Python list

Parameters:

columnsstr, optional: Controls which attributes to include in the list, by default None

Returns:

list: Records as a Python list

toSparseCoo(column: str = 'level') → coo_matrix | None#

Convert column to a sparse COOrdinate numpy.array format

Parameters:

columnstr, optional: The column to convert, by default “level”

Returns:

coo_matrix, optional: A column in coo_matrix format

toValue(column: str | None = None) → float#

Convenience method to return symbol records as a Python float. Only possible with scalar symbols

Parameters:

columnstr, optional: Attribute can be specified with column argument, by default None

Returns:

float: Value of the symbol

whereMax(column: str | None = None) → List[str]#

Find the domain entry of records with a maximum value (return first instance only)

Parameters:

columnstr, optional: Columns to find maximum values in, by default None

Returns:

List[str]: List of symbol names where maximum values exist

whereMaxAbs(column: str | None = None) → List[str]#

Find the domain entry of records with a maximum absolute value (return first instance only)

Parameters:

columnstr, optional: Columns to find maximum absolute values in, by default None

Returns:

List[str]: List of symbol names where maximum absolute values exist

whereMin(column: str | None = None) → List[str]#

Find the domain entry of records with a minimum value (return first instance only)

Parameters:

columnstr, optional: Columns to find minimum values in, by default None

Returns:

List[str]: List of symbol names where minimum values exist

property records#

Records of the Equation

Returns:

DataFrame

Examples

>>> from gamspy import Container, Parameter, Variable, Equation, Model
>>> m = Container()
>>> x1 = Variable(m, "x1", type="Positive")
>>> x2 = Variable(m, "x2", type="Positive")
>>> z = Variable(m, "z")
>>> eq = Equation(m, "eq")
>>> eq[...] = 2*x1 + 3*x2 <= 10
>>> solved_model = Model(m, "my_model", equations=[eq], objective=10*x1 + 6*x2, sense="MAX").solve()
>>> eq.toValue()
np.float64(10.0)

setRecords(records: Any, uels_on_axes: bool = False) → None[source]#

Main convenience method to set standard pandas.DataFrame formatted records. If uels_on_axes=True setRecords will assume that all domain information is contained in the axes of the pandas object – data will be flattened (if necessary).

Parameters:

recordsAny
uels_on_axesbool, optional

Examples

>>> from gamspy import Container, Variable, Equation
>>> m = Container()
>>> x1 = Variable(m, "x1", type="Positive")
>>> x2 = Variable(m, "x2", type="Positive")
>>> z = Variable(m, "z")
>>> eq = Equation(m, "eq")
>>> eq.setRecords(5)
>>> eq.toValue()
np.float64(5.0)

property type#

The type of equation; 3. ‘regular’ – equal, less than or greater than 4. ‘nonbinding’, ‘N’, or ‘=N=’ – nonbinding relationship 5. ‘cone’, ‘C’, or ‘=C=’ – cone equation 6. ‘external’, ‘X’, or ‘=X=’ – external equation 7. ‘boolean’, ‘B’, or ‘=B=’ – boolean equation

Returns:

str: The type of equation

gamsRepr() → str[source]#

Representation of this Equation in GAMS language.

Returns:

str

Examples

>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, "i", records=['i1','i2'])
>>> e = gp.Equation(m, "e", domain=[i])
>>> e.gamsRepr()
'e'

getDeclaration() → str[source]#

Declaration of the Equation in GAMS

Returns:

str

Examples

>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, "i", records=['i1','i2'])
>>> a = gp.Parameter(m, "a", [i], records=[['i1',1],['i2',2]])
>>> v = gp.Variable(m, "v", domain=[i])
>>> e = gp.Equation(m, "e", domain=[i])
>>> e.getDeclaration()
'Equation e(i);'

getDefinition() → str[source]#

Definition of the Equation in GAMS

Returns:

str

Examples

>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, "i", records=['i1','i2'])
>>> a = gp.Parameter(m, "a", [i], records=[['i1',1],['i2',2]])
>>> v = gp.Variable(m, "v", domain=[i])
>>> e = gp.Equation(m, "e", domain=[i])
>>> e[i] = a[i] <= v[i]
>>> e.getDefinition()
'e(i) .. a(i) =l= v(i);'

class gamspy.EquationType(value)[source]#

Bases: Enum

An enumeration.

REGULAR = 'REGULAR'#

NONBINDING = 'NONBINDING'#

EXTERNAL = 'EXTERNAL'#

CONE = 'CONE'#

BOOLEAN = 'BOOLEAN'#

classmethod values()[source]#: Convenience function to return all values of enum