def _build_constraint_functions(variables,
constraints,
include_hess=False,
parameters=None,
cse=True):
if parameters is None:
parameters = []
else:
parameters = [wrap_symbol_symengine(p) for p in parameters]
variables = tuple(variables)
wrt = variables
parameters = tuple(parameters)
constraint__func, jacobian_func, hessian_func = None, None, None
inp = sympify(variables + parameters)
graph = sympify(constraints)
constraint_func = lambdify(inp, [graph], backend='llvm', cse=cse)
grad_graphs = list(list(c.diff(w) for w in wrt) for c in graph)
jacobian_func = lambdify(inp, grad_graphs, backend='llvm', cse=cse)
if include_hess:
hess_graphs = list(
list(list(g.diff(w) for w in wrt) for g in c) for c in grad_graphs)
hessian_func = lambdify(inp, hess_graphs, backend='llvm', cse=cse)
return ConstraintFunctions(cons_func=constraint_func,
cons_jac=jacobian_func,
cons_hess=hessian_func)
def build_constraint_functions(variables,
constraints,
parameters=None,
func_options=None,
jac_options=None,
hess_options=None):
"""Build callables functions for the constraints, constraint Jacobian, and constraint Hessian.
Parameters
----------
variables : List[sympy.core.symbol.Symbol]
Free variables in the sympy_graph. By convention these are usually all
instances of StateVariables.
constraints : List[sympy.core.expr.Expr]
List of SymPy expression to compile
parameters : Optional[List[sympy.core.symbol.Symbol]]
Free variables in the sympy_graph. These are typically external
parameters that are controlled by the user.
func_options : None, optional
Options to pass to ``lambdify`` when compiling the function.
jac_options : Optional[Dict[str, str]]
Options to pass to ``lambdify`` when compiling the Jacobian.
hess_options : Optional[Dict[str, str]]
Options to pass to ``lambdify`` when compiling Hessian.
Returns
-------
ConstraintFunctions
Notes
-----
Default options for compiling the function, gradient and Hessian are defined by ``_get_lambdify_options``.
"""
if parameters is None:
parameters = []
else:
parameters = [wrap_symbol_symengine(p) for p in parameters]
variables = tuple(variables)
wrt = variables
parameters = tuple(parameters)
constraint_func, jacobian_func, hessian_func = None, None, None
inp = sympify(variables + parameters)
graph = sympify(constraints)
constraint_func = lambdify(inp, [graph],
**_get_lambidfy_options(func_options))
grad_graphs = list(list(c.diff(w) for w in wrt) for c in graph)
jacobian_func = lambdify(inp, grad_graphs,
**_get_lambidfy_options(jac_options))
hess_graphs = list(
list(list(g.diff(w) for w in wrt) for g in c) for c in grad_graphs)
hessian_func = lambdify(inp, hess_graphs,
**_get_lambidfy_options(hess_options))
return ConstraintFunctions(cons_func=constraint_func,
cons_jac=jacobian_func,
cons_hess=hessian_func)
def _build_constraint_functions(variables, constraints, include_hess=False, parameters=None, cse=True):
if parameters is None:
parameters = []
else:
parameters = [wrap_symbol_symengine(p) for p in parameters]
variables = tuple(variables)
wrt = variables
parameters = tuple(parameters)
constraint__func, jacobian_func, hessian_func = None, None, None
inp = sympify(variables + parameters)
graph = sympify(constraints)
constraint_func = lambdify(inp, [graph], backend='llvm', cse=cse)
grad_graphs = list(list(c.diff(w) for w in wrt) for c in graph)
jacobian_func = lambdify(inp, grad_graphs, backend='llvm', cse=cse)
if include_hess:
hess_graphs = list(list(list(g.diff(w) for w in wrt) for g in c) for c in grad_graphs)
hessian_func = lambdify(inp, hess_graphs, backend='llvm', cse=cse)
return ConstraintFunctions(cons_func=constraint_func, cons_jac=jacobian_func, cons_hess=hessian_func)
def build_functions(sympy_graph,
variables,
parameters=None,
wrt=None,
include_obj=True,
include_grad=False,
include_hess=False,
cse=True):
if wrt is None:
wrt = sympify(tuple(variables))
if parameters is None:
parameters = []
else:
parameters = [wrap_symbol_symengine(p) for p in parameters]
variables = tuple(variables)
parameters = tuple(parameters)
func, grad, hess = None, None, None
inp = sympify(variables + parameters)
graph = sympify(sympy_graph)
if count_ops(graph) > BACKEND_OPS_THRESHOLD:
backend = 'lambda'
else:
backend = 'llvm'
# TODO: did not replace zoo with oo
if include_obj:
func = lambdify(inp, [graph], backend=backend, cse=cse)
if include_grad or include_hess:
grad_graphs = list(graph.diff(w) for w in wrt)
grad_ops = sum(count_ops(x) for x in grad_graphs)
if grad_ops > BACKEND_OPS_THRESHOLD:
grad_backend = 'lambda'
else:
grad_backend = 'llvm'
if include_grad:
grad = lambdify(inp, grad_graphs, backend=grad_backend, cse=cse)
if include_hess:
hess_graphs = list(
list(g.diff(w) for w in wrt) for g in grad_graphs)
# Hessians are hard-coded to always use the lambda backend, for performance
hess = lambdify(inp, hess_graphs, backend='lambda', cse=cse)
return BuildFunctionsResult(func=func, grad=grad, hess=hess)
def build_functions(sympy_graph, variables, parameters=None, wrt=None, include_obj=True, include_grad=False, include_hess=False, cse=True):
if wrt is None:
wrt = sympify(tuple(variables))
if parameters is None:
parameters = []
else:
parameters = [wrap_symbol_symengine(p) for p in parameters]
variables = tuple(variables)
parameters = tuple(parameters)
func, grad, hess = None, None, None
inp = sympify(variables + parameters)
graph = sympify(sympy_graph)
# TODO: did not replace zoo with oo
if include_obj:
func = lambdify(inp, [graph], backend='llvm', cse=cse)
if include_grad or include_hess:
grad_graphs = list(graph.diff(w) for w in wrt)
if include_grad:
grad = lambdify(inp, grad_graphs, backend='llvm', cse=cse)
if include_hess:
hess_graphs = list(list(g.diff(w) for w in wrt) for g in grad_graphs)
hess = lambdify(inp, hess_graphs, backend='llvm', cse=cse)
return BuildFunctionsResult(func=func, grad=grad, hess=hess)
def build_functions(sympy_graph,
variables,
parameters=None,
wrt=None,
include_obj=True,
include_grad=False,
include_hess=False,
func_options=None,
grad_options=None,
hess_options=None):
"""Build function, gradient, and Hessian callables of the sympy_graph.
Parameters
----------
sympy_graph : sympy.core.expr.Expr
SymPy expression to compile,
:math:`f(x) : \mathbb{R}^{n} \\rightarrow \mathbb{R}`,
which will corresponds to ``sympy_graph(variables+parameters)``
variables : List[sympy.core.symbol.Symbol]
Free variables in the sympy_graph. By convention these are usually all
instances of StateVariables.
parameters : Optional[List[sympy.core.symbol.Symbol]]
Free variables in the sympy_graph. These are typically external
parameters that are controlled by the user.
wrt : Optional[List[sympy.core.symbol.Symbol]]
Variables to differentiate *with respect to* for the gradient and
Hessian callables. If None, will fall back to ``variables``.
include_obj : Optional[bool]
Whether to build the sympy_graph callable,
:math:`f(x) : \mathbb{R}^{n} \\rightarrow \mathbb{R}`
include_grad : Optional[bool]
Whether to build the gradient callable,
:math:`\\pmb{g}(x) = \\nabla f(x) : \mathbb{R}^{n} \\rightarrow \mathbb{R}^{n}`
include_hess : Optional[bool]
Whether to build the Hessian callable,
:math:`\mathbb{H}(x) = \\nabla^2 f(x) : \mathbb{R}^{n} \\rightarrow \mathbb{R}^{n \\times n}`
func_options : Optional[Dict[str, str]]
Options to pass to ``lambdify`` when compiling the function.
grad_options : Optional[Dict[str, str]]
Options to pass to ``lambdify`` when compiling the gradient.
hess_options : Optional[Dict[str, str]]
Options to pass to ``lambdify`` when compiling Hessian.
Returns
-------
BuildFunctionsResult
Notes
-----
Default options for compiling the function, gradient and Hessian are defined by ``_get_lambdify_options``.
"""
if wrt is None:
wrt = sympify(tuple(variables))
if parameters is None:
parameters = []
else:
parameters = [wrap_symbol_symengine(p) for p in parameters]
variables = tuple(variables)
parameters = tuple(parameters)
func, grad, hess = None, None, None
# Replace complex infinity (zoo) with real infinity because SymEngine
# cannot lambdify complex infinity. We also replace in the derivatives in
# case some differentiation would produce a complex infinity. The
# replacement is assumed to be cheap enough that it's safer to replace the
# complex values and pay the minor time penalty.
inp = sympify(variables + parameters)
graph = sympify(sympy_graph).xreplace({zoo: oo})
func = lambdify(inp, [graph], **_get_lambidfy_options(func_options))
if include_grad or include_hess:
grad_graphs = list(graph.diff(w).xreplace({zoo: oo}) for w in wrt)
if include_grad:
grad = lambdify(inp, grad_graphs,
**_get_lambidfy_options(grad_options))
if include_hess:
hess_graphs = list(
list(g.diff(w).xreplace({zoo: oo}) for w in wrt)
for g in grad_graphs)
hess = lambdify(inp, hess_graphs,
**_get_lambidfy_options(hess_options))
return BuildFunctionsResult(func=func, grad=grad, hess=hess)
def build_constraint_functions(variables,
constraints,
parameters=None,
func_options=None,
jac_options=None,
hess_options=None):
"""Build callables functions for the constraints, constraint Jacobian, and constraint Hessian.
Parameters
----------
variables : List[sympy.core.symbol.Symbol]
Free variables in the sympy_graph. By convention these are usually all
instances of StateVariables.
constraints : List[sympy.core.expr.Expr]
List of SymPy expression to compile
parameters : Optional[List[sympy.core.symbol.Symbol]]
Free variables in the sympy_graph. These are typically external
parameters that are controlled by the user.
func_options : None, optional
Options to pass to ``lambdify`` when compiling the function.
jac_options : Optional[Dict[str, str]]
Options to pass to ``lambdify`` when compiling the Jacobian.
hess_options : Optional[Dict[str, str]]
Options to pass to ``lambdify`` when compiling Hessian.
Returns
-------
ConstraintFunctions
Notes
-----
Default options for compiling the function, gradient and Hessian are defined by ``_get_lambdify_options``.
"""
if parameters is None:
parameters = []
else:
parameters = [wrap_symbol_symengine(p) for p in parameters]
variables = tuple(variables)
wrt = variables
parameters = tuple(parameters)
constraint_func, jacobian_func, hessian_func = None, None, None
# Replace complex infinity (zoo) with real infinity because SymEngine
# cannot lambdify complex infinity. We also replace in the derivatives in
# case some differentiation would produce a complex infinity. The
# replacement is assumed to be cheap enough that it's safer to replace the
# complex values and pay the minor time penalty.
inp = sympify(variables + parameters)
graph = sympify([f.xreplace({zoo: oo}) for f in constraints])
constraint_func = lambdify(inp, [graph],
**_get_lambidfy_options(func_options))
grad_graphs = list(
list(c.diff(w).xreplace({zoo: oo}) for w in wrt) for c in graph)
jacobian_func = lambdify(inp, grad_graphs,
**_get_lambidfy_options(jac_options))
hess_graphs = list(
list(list(g.diff(w).xreplace({zoo: oo}) for w in wrt) for g in c)
for c in grad_graphs)
hessian_func = lambdify(inp, hess_graphs,
**_get_lambidfy_options(hess_options))
return ConstraintFunctions(cons_func=constraint_func,
cons_jac=jacobian_func,
cons_hess=hessian_func)
