def get_numeric_incidence_matrix(variables, constraints):
"""
This function gets the numeric incidence matrix (Jacobian) of Pyomo
constraints with respect to variables.
"""
# NOTE: There are several ways to get a numeric incidence matrix
# from a Pyomo model. This function implements a somewhat roundabout
# method, which is to construct a dummy Block with the necessary
# variables and constraints, then construct a PyNumero PyomoNLP
# from the block and have PyNumero evaluate the desired Jacobian
# via ASL.
comps = list(variables) + list(constraints)
_check_unindexed(comps)
M, N = len(constraints), len(variables)
_block = Block()
_block.construct()
_block.obj = Objective(expr=0)
_block.vars = Reference(variables)
_block.cons = Reference(constraints)
var_set = ComponentSet(variables)
other_vars = []
for con in constraints:
for var in identify_variables(con.body, include_fixed=False):
# Fixed vars will be ignored by the nl file write, so
# there is no point to including them here.
# A different method of assembling this matrix, e.g.
# Pyomo's automatic differentiation, could support taking
# derivatives with respect to fixed variables.
if var not in var_set:
other_vars.append(var)
var_set.add(var)
# These variables are necessary due to the nl writer's philosophy
# about what constitutes a model. Note that we take derivatives with
# respect to them even though this is not necessary. We could fix them
# here to avoid doing this extra work, but that would alter the user's
# model, which we would rather not do.
_block.other_vars = Reference(other_vars)
_nlp = PyomoNLP(_block)
return _nlp.extract_submatrix_jacobian(variables, constraints)
def create_subsystem_block(constraints, variables=None, include_fixed=False):
""" This function creates a block to serve as a subsystem with the
specified variables and constraints. To satisfy certain writers, other
variables that appear in the constraints must be added to the block as
well. We call these the "input vars." They may be thought of as
parameters in the subsystem, but we do not fix them here as it is not
obvious that this is desired.
Arguments
---------
constraints: List
List of Pyomo constraint data objects
variables: List
List of Pyomo var data objects
include_fixed: Bool
Indicates whether fixed variables should be attached to the block.
This is useful if they may be unfixed at some point.
Returns
-------
Block containing references to the specified constraints and variables,
as well as other variables present in the constraints
"""
if variables is None:
variables = []
block = Block(concrete=True)
block.vars = Reference(variables)
block.cons = Reference(constraints)
var_set = ComponentSet(variables)
input_vars = []
for con in constraints:
for var in identify_variables(con.expr, include_fixed=include_fixed):
if var not in var_set:
input_vars.append(var)
var_set.add(var)
block.input_vars = Reference(input_vars)
add_local_external_functions(block)
return block