Implementations

class condor.implementations.simple.ExplicitSystem(model_instance)

Implementation for ExplicitSystem model.

No Options expected.

class condor.implementations.simple.ExternalSolverModel(model_instance)

Implementation for External Solver models.

No Options expected.

class condor.implementations.sgm_trajectory.TrajectoryAnalysis(model_instance)

Implementation for TrajectoryAnalysis model.

Options:

  • state_atol (float) – absolute tolerance for forward evalaution

  • state_rtol (float) – relative tolerance for forward evalaution

  • state_adaptive_max_step_size (float) – actually a minimum number of steps per time-defined segment for the forward evaluation

  • state_max_step_size (float) – maximum step size for the forward evaluation

  • state_solver (TrajectoryAnalysis.Solver) – enum member for solver type

  • adjoint_* – same as above, for the adjoint solution

class Solver(*values)
CVODE = 1

currently unsupported

dopri5 = 2
dop853 = 3
construct(model, state_atol=1e-12, state_rtol=1e-06, state_adaptive_max_step_size=0.0, state_max_step_size=0, adjoint_atol=1e-12, adjoint_rtol=1e-06, adjoint_adaptive_max_step_size=4.0, adjoint_max_step_size=0, state_solver=Solver.dopri5, adjoint_solver=Solver.dopri5)

Iterative Solvers

class condor.implementations.iterative.AlgebraicSystem(model_instance)

Implementation for AlgebraicSystem model.

Options:

  • atol (float) – absolute tolerance (default 1e-12)

  • rtol (float) – relative tolerance (default 1e-12)

  • warm_start (bool) – flag indicating whether subsequent calls should initialize the variable values to the last-run solution (default True)

  • max_iter (int) – maximum number of iterations before terminating solver (default 100)

  • error_on_fail (bool) – flag indicating whether to raise an error if the solver fails to converge within specified tolerance before reaching max_iter (default False)

construct(model, atol=1e-12, rtol=1e-12, warm_start=True, exact_hessian=True, max_iter=100, re_initialize=None, default_initializer=0.0, error_on_fail=False)
class condor.implementations.iterative.OptimizationProblem(model_instance)

Implementation base class for OptimizationProblem model.

Options:

  • init_callback (callable) – callback with signature , called when an optimization problem is evalauted. Once when embedded or every time as a standalone.

  • iter_callback (callable) – callback with signature , called at each iteration of the CasadiNlpsolImplementation (only IPOPT) and SciPyBase subclass optimization implementaitons.

make_warm_start(x0=None, lam_g0=None, lam_x0=None)
default_options = {}
construct(model, iter_callback=None, init_callback=None, **options)
load_initializer(model_instance)
class condor.implementations.iterative.CasadiNlpsolImplementation(model_instance)

Implementation layer for casadi nlpsol for OptimizationProblem models.

Options:

  • method (CasadiNlpsolImplementation.Method) – value from method enum to specify supported methods

  • exact_hessian (bool) – flag to use second order gradient information; use limited Broyden update otherwise

  • calc_lam_x (bool) – flag to calculate the lagrange multipliers solution, used for IPOPT to perform a true warm start

  • **options – remaining keyword arguments are passed to casadi’s nlpsol’s constructor’s solver-specific options argument. See method_default_options for the defaults

class Method(*values)
ipopt = 1
snopt = 2

currently unsupported

qrsqp = 3
fatrop = 4
method_strings = {Method.fatrop: 'fatrop', Method.ipopt: 'ipopt', Method.qrsqp: 'sqpmethod', Method.snopt: 'snopt'}
method_default_options = {Method.ipopt: {'sb': 'yes', 'warm_start_init_point': 'yes'}, Method.snopt: {'warm_start_init_point': False}}
property default_options

derived property to get a copy of method_default_options or an empty dict

construct(model, exact_hessian=True, method=Method.ipopt, calc_lam_x=False, **options)
run_optimizer(model_instance)
class condor.implementations.iterative.ScipyMinimizeBase(model_instance)

Base implementation class for SciPy minimize for OptimizationProblem models.

Options:

**options – keyword options are passed directly to scipy.minimize’s options keyword argument

construct(model, **options)
prepare_constraints(extra_args)
run_optimizer(model_instance)
class condor.implementations.iterative.ScipyCG(model_instance)
method_string = 'CG'
class condor.implementations.iterative.ScipySLSQP(model_instance)
method_string = 'SLSQP'
construct(model, *args, **kwargs)
prepare_constraints(extra_args)
class condor.implementations.iterative.ScipyTrustConstr(model_instance)
method_string = 'trust-constr'
construct(model, exact_hessian=True, keep_feasible=True, **options)
prepare_constraints(extra_args)