Common Model Interface¶

Generally speaking, the following APIs are common to all optimizers except for adding constraint because different optimizers may support different types of constraints.

Model¶

Get/set model attributes¶

model.set_model_attribute(attr, value)¶

set the value of a model attribute

Parameters:

attr (pyoptinterface.ModelAttribute) – the attribute to set
value – the value to set

model.get_model_attribute(attr)¶

get the value of a model attribute

Parameters:: attr (pyoptinterface.ModelAttribute) – the attribute to get
Returns:: the value of the attribute

Variable¶

Add a variable to the model¶

model.add_variable([lb=-inf, ub=+inf, domain=pyoptinterface.VariableDomain.Continuous, name=""])¶

add a variable to the model

Parameters:

lb (float) – the lower bound of the variable, optional, defaults to \(-\infty\)
ub (float) – the upper bound of the variable, optional, defaults to \(+\infty\)
domain (pyoptinterface.VariableDomain) – the domain of the variable, optional, defaults to continuous
name (str) – the name of the variable, optional

Returns:

the handle of the variable

Add multidimensional variables to the model as `pyoptinterface.tupledict`¶

model.add_variables(*coords[, lb=-inf, ub=+inf, domain=pyoptinterface.VariableDomain.Continuous, name=""])¶

add a multidimensional variable to the model

Parameters:

coords – the coordinates of the variable, can be a list of Iterables
lb (float) – the lower bound of the variable, optional, defaults to \(-\infty\)
ub (float) – the upper bound of the variable, optional, defaults to \(+\infty\)
domain (pyoptinterface.VariableDomain) – the domain of the variable, optional, defaults to continuous
name (str) – the name of the variable, optional

Returns:

the multi-dimensional variable

Return type:

pyoptinterface.tupledict

Add multidimensional variables to the model as `numpy.ndarray`¶

model.add_m_variables(shape[, lb=-inf, ub=+inf, domain=pyoptinterface.VariableDomain.Continuous, name=""])¶

add a multidimensional variable to the model as numpy.ndarray

Parameters:

shape – the shape of the variable, can be a tuple of integers or an integer
lb (float) – the lower bound of the variable, optional, defaults to \(-\infty\)
ub (float) – the upper bound of the variable, optional, defaults to \(+\infty\)
domain (pyoptinterface.VariableDomain) – the domain of the variable, optional, defaults to continuous
name (str) – the name of the variable, optional

Returns:

the multidimensional variable

Return type:

numpy.ndarray

Get/set variable attributes¶

model.set_variable_attribute(var, attr, value)¶

set the value of a variable attribute

Parameters:

var – the handle of the variable
attr (pyoptinterface.VariableAttribute) – the attribute to set
value – the value to set

model.get_variable_attribute(var, attr)¶

get the value of a variable attribute

Parameters:

var – the handle of the variable
attr (pyoptinterface.VariableAttribute) – the attribute to get

Returns:

the value of the attribute

Delete variable¶

model.delete_variable(var)¶

delete a variable from the model

Parameters:: var – the handle of the variable

model.is_variable_active(var)¶

query whether a variable is active

Parameters:: var – the handle of the variable
Returns:: whether the variable is active
Return type:: bool

Modify the bounds of variable¶

model.set_variable_bounds(var, lb, ub)¶

set the lower and upper bounds of a variable

Parameters:

var – the handle of the variable
lb (float) – the new lower bound value
ub (float) – the new upper bound value

Expression¶

Get the value of an expression (including variable)¶

model.get_value(expr_or_var)¶

get the value of an expression or a variable after optimization

Parameters:: expr_or_var – the handle of the expression or the variable
Returns:: the value of the expression or the variable
Return type:: float

Pretty print expression (including variable)¶

model.pprint(expr_or_var)¶

pretty print an expression in a human-readable format

Parameters:: expr_or_var – the handle of the expression or the variable
Returns:: the human-readable format of the expression
Return type:: str

Constraint¶

Add a constraint to the model¶

Add linear constraints as matrix form to the model¶

model.add_m_linear_constraints(A, vars, sense, b[, name=""])¶

add linear constraints as matrix form to the model \(Ax \le b\) or \(Ax = b\) or \(Ax \ge b\)

Parameters:

A – the matrix of coefficients, can be a dense numpy.ndarray or a sparse matrix scipy.sparse.sparray
vars – the variables in the constraints, can be a list or a 1-d numpy.ndarray returned by add_m_variables
sense (pyoptinterface.ConstraintSense) – the sense of the constraints
b – the right-hand side of the constraints, should be a 1-d numpy.ndarray
name (str) – the name of the constraints, optional

Returns:

the handles of linear constraints

Return type:

numpy.ndarray

Get/set constraint attributes¶

model.set_constraint_attribute(con, attr, value)¶

set the value of a constraint attribute

Parameters:

con – the handle of the constraint
attr (pyoptinterface.ConstraintAttribute) – the attribute to set
value – the value to set

model.get_constraint_attribute(con, attr)¶

get the value of a constraint attribute

Parameters:

con – the handle of the constraint
attr (pyoptinterface.ConstraintAttribute) – the attribute to get

Returns:

the value of the attribute

Delete constraint¶

model.delete_constraint(con)¶

delete a constraint from the model

Parameters:: con – the handle of the constraint

model.is_constraint_active(con)¶

query whether a constraint is active

Parameters:: con – the handle of the constraint
Returns:: whether the variable is active
Return type:: bool

Modify constraint¶

model.set_normalized_rhs(con, value)¶

set the right-hand side of a normalized constraint

Parameters:

con – the handle of the constraint
value – the new right-hand side value

model.get_normalized_rhs(con)¶

get the right-hand side of a normalized constraint

Parameters:: con – the handle of the constraint
Returns:: the right-hand side value

model.set_normalized_coefficient(con, var, value)¶

set the coefficient of a variable in a normalized constraint

Parameters:

con – the handle of the constraint
var – the handle of the variable
value – the new coefficient value

model.get_normalized_coefficient(con, var)¶

get the coefficient of a variable in a normalized constraint

Parameters:

con – the handle of the constraint
var – the handle of the variable

Returns:

the coefficient value

Objective¶

Set the objective function¶

model.set_objective(expr[, sense=pyoptinterface.ObjectiveSense.Minimize])¶

set the objective function of the model

Parameters:

expr – the handle of the expression
sense (pyoptinterface.ObjectiveSense) – the sense of the objective function (Minimize/Maximize), defaults to Minimize

Modify the linear part of the objective function¶

model.set_objective_coefficient(var, value)¶

modify the coefficient of a variable in the linear part of the objective function

Parameters:

var – the handle of the variable
value (float) – the new coefficient value

model.get_objective_coefficient(var)¶

get the coefficient of a variable in the linear part of the objective function

Parameters:: var – the handle of the variable
Returns:: the coefficient value

Common Model Interface¶

Model¶

Get/set model attributes¶

Variable¶

Add a variable to the model¶

Add multidimensional variables to the model as pyoptinterface.tupledict¶

Add multidimensional variables to the model as numpy.ndarray¶

Get/set variable attributes¶

Delete variable¶

Modify the bounds of variable¶

Expression¶

Get the value of an expression (including variable)¶

Pretty print expression (including variable)¶

Constraint¶

Add a constraint to the model¶

Add linear constraints as matrix form to the model¶

Get/set constraint attributes¶

Delete constraint¶

Modify constraint¶

Objective¶

Set the objective function¶

Modify the linear part of the objective function¶

Add multidimensional variables to the model as `pyoptinterface.tupledict`¶

Add multidimensional variables to the model as `numpy.ndarray`¶