Source code for openmdao.solvers.nonlinear.broyden

"""
Define the BroydenSolver class.

Based on implementation in Scipy via OpenMDAO 0.8x with improvements based on NPSS solver.
"""
from __future__ import print_function
from six.moves import range

import numpy as np

from openmdao.recorders.recording_iteration_stack import Recording
from openmdao.solvers.solver import NonlinearSolver
from openmdao.utils.class_util import overrides_method
from openmdao.utils.general_utils import simple_warning, warn_deprecation
from openmdao.utils.mpi import MPI
from openmdao.vectors.vector import INT_DTYPE


CITATION = """@ARTICLE{
              Broyden1965ACo,
              AUTHOR = "C. Broyden",
              TITLE = "A Class of Methods for Solving Nonlinear Simultaneous Equations",
              JOURNAL = "Mathematics of Computation",
              VOLUME = "19",
              YEAR = "1965",
              PAGES = "577--593",
              REFERRED = "[Coleman1996SaE]."
              }"""


[docs]class BroydenSolver(NonlinearSolver): """ Broyden solver. Attributes ---------- delta_fxm : ndarray Most recent change in residual vector. delta_xm : ndarray Most recent change in state vector. fxm : ndarray Most recent residual. Gm : ndarray Most recent Jacobian matrix. linear_solver : LinearSolver Linear solver to use for calculating inverse Jacobian. linesearch : NonlinearSolver Line search algorithm. Default is None for no line search. size : int Total length of the states being solved. xm : ndarray Most recent state. _idx : dict Cache of vector indices for each state name. _computed_jacobians : int Number of computed jacobians. _converge_failures : int Number of consecutive iterations that failed to converge to the tol definied in options. _full_inverse : bool When True, Broyden considers the whole vector rather than a list of states. _recompute_jacobian : bool Flag that becomes True when Broyden detects it needs to recompute the inverse Jacobian. """ SOLVER = 'BROYDEN'
[docs] def __init__(self, **kwargs): """ Initialize all attributes. Parameters ---------- **kwargs : dict options dictionary. """ super(BroydenSolver, self).__init__(**kwargs) # Slot for linear solver self.linear_solver = None # Slot for linesearch self.linesearch = None self.cite = CITATION self.size = 0 self._idx = {} self._recompute_jacobian = True self.Gm = None self.xm = None self.fxm = None self.delta_xm = None self.delta_fxm = None self._converge_failures = 0 self._computed_jacobians = 0 # This gets set to True if the user doesn't declare any states. self._full_inverse = False
def _declare_options(self): """ Declare options before kwargs are processed in the init method. """ super(BroydenSolver, self)._declare_options() self.options.declare('alpha', default=0.4, desc="Value to scale the starting Jacobian, which is Identity. This " "option does nothing if you compute the initial Jacobian " "instead.") self.options.declare('compute_jacobian', types=bool, default=True, desc="When True, compute an initial Jacobian, otherwise start " "with Identity scaled by alpha. Further Jacobians may also be " "computed depending on the other options.") self.options.declare('converge_limit', default=1.0, desc="Ratio of current residual to previous residual above which the " "convergence is considered a failure. The Jacobian will be " "regenerated once this condition has been reached a number of " "consecutive times as specified in max_converge_failures.") self.options.declare('cs_reconverge', types=bool, default=True, desc='When True, when this driver solves under a complex step, nudge ' 'the Solution vector by a small amount so that it reconverges.') self.options.declare('diverge_limit', default=2.0, desc="Ratio of current residual to previous residual above which the " "Jacobian will be immediately regenerated.") self.options.declare('max_converge_failures', default=3, desc="The number of convergence failures before regenerating the " "Jacobian.") self.options.declare('max_jacobians', default=10, desc="Maximum number of jacobians to compute.") self.options.declare('state_vars', [], desc="List of the state-variable/residuals that " "are to be solved here.") self.options.declare('update_broyden', default=True, desc="Flag controls whether to perform Broyden update to the " "Jacobian. There are some applications where it may be useful " "to turn this off.") self.options.declare('reraise_child_analysiserror', types=bool, default=True, desc='When the option is true, a solver will reraise any ' 'AnalysisError that arises during subsolve; when false, it will ' 'continue solving.') self.supports['gradients'] = True self.supports['implicit_components'] = True def _setup_solvers(self, system, depth): """ Assign system instance, set depth, and optionally perform setup. Parameters ---------- system : <System> Pointer to the owning system. depth : int Depth of the current system (already incremented). """ super(BroydenSolver, self)._setup_solvers(system, depth) self._recompute_jacobian = True self._computed_jacobians = 0 iproc = system.comm.rank rank = MPI.COMM_WORLD.rank if MPI is not None else 0 self._disallow_discrete_outputs() if self.options._dict['reraise_child_analysiserror']['value']: pathname = self._system().pathname if pathname: pathname += ': ' msg = ("Deprecation warning: In V 3.x, reraise_child_analysiserror will default to " "False.") if rank == 0: warn_deprecation(pathname + msg) if self.linear_solver is not None: self.linear_solver._setup_solvers(system, self._depth + 1) else: self.linear_solver = system.linear_solver if self.linesearch is not None: self.linesearch._setup_solvers(system, self._depth + 1) self.linesearch._do_subsolve = True else: # In OpenMDAO 3.x, we will be making BoundsEnforceLS the default line search. # This deprecation warning is to prepare users for the change. pathname = system.pathname if pathname: pathname += ': ' msg = 'Deprecation warning: In V 3.0, the default Broyden solver setup will change ' + \ 'to use the BoundsEnforceLS line search.' warn_deprecation(pathname + msg) self._disallow_distrib_solve() states = self.options['state_vars'] prom2abs = system._var_allprocs_prom2abs_list['output'] # Check names of states. bad_names = [name for name in states if name not in prom2abs] if len(bad_names) > 0: msg = "{}: The following variable names were not found: {}" raise ValueError(msg.format(self.msginfo, ', '.join(bad_names))) # Size linear system if len(states) > 0: # User has specified states, so we must size them. n = 0 sizes = system._var_allprocs_abs2meta for i, name in enumerate(states): size = sizes[prom2abs[name][0]]['global_size'] self._idx[name] = (n, n + size) n += size else: # Full system size. self._full_inverse = True n = np.sum(system._owned_sizes) self.size = n self.Gm = np.empty((n, n)) self.xm = np.empty((n, )) self.fxm = np.empty((n, )) self.delta_xm = None self.delta_fxm = None if self._full_inverse: # Can only use DirectSolver here. from openmdao.solvers.linear.direct import DirectSolver if not isinstance(self.linear_solver, DirectSolver): msg = "{}: Linear solver must be DirectSolver when solving the full model." raise ValueError(msg.format(self.msginfo, ', '.join(bad_names))) return # Always look for states that aren't being solved so we can warn the user. def sys_recurse(system, all_states): subs = system._subsystems_myproc if len(subs) == 0: # Skip implicit components that appear to solve themselves. from openmdao.core.implicitcomponent import ImplicitComponent if overrides_method('solve_nonlinear', system, ImplicitComponent): return all_states.extend(system._list_states()) else: for subsys in subs: sub_nl = subsys.nonlinear_solver if sub_nl and sub_nl.supports['implicit_components']: continue sys_recurse(subsys, all_states) all_states = [] sys_recurse(system, all_states) all_states = [system._var_abs2prom['output'][name] for name in all_states] missing = set(all_states).difference(states) if len(missing) > 0: msg = "The following states are not covered by a solver, and may have been " + \ "omitted from the BroydenSolver 'state_vars': " msg += ', '.join(sorted(missing)) simple_warning(msg) def _assembled_jac_solver_iter(self): """ Return a generator of linear solvers using assembled jacs. """ if self.linear_solver is not None: for s in self.linear_solver._assembled_jac_solver_iter(): yield s def _set_solver_print(self, level=2, type_='all'): """ Control printing for solvers and subsolvers in the model. Parameters ---------- level : int iprint level. Set to 2 to print residuals each iteration; set to 1 to print just the iteration totals; set to 0 to disable all printing except for failures, and set to -1 to disable all printing including failures. type_ : str Type of solver to set: 'LN' for linear, 'NL' for nonlinear, or 'all' for all. """ super(BroydenSolver, self)._set_solver_print(level=level, type_=type_) if self.linear_solver is not None and type_ != 'NL': self.linear_solver._set_solver_print(level=level, type_=type_) if self.linesearch is not None: self.linesearch._set_solver_print(level=level, type_=type_) def _linearize(self): """ Perform any required linearization operations such as matrix factorization. """ if self.linear_solver is not None: self.linear_solver._linearize() if self.linesearch is not None: self.linesearch._linearize() def _iter_initialize(self): """ Perform any necessary pre-processing operations. Returns ------- float Initial relative error in the user-specified residuals. float Initial absolute error in the user-specified residuals. """ system = self._system() if self.options['debug_print']: self._err_cache['inputs'] = system._inputs._copy_views() self._err_cache['outputs'] = system._outputs._copy_views() # Convert local storage if we are under complex step. if system.under_complex_step: self.Gm = self.Gm.astype(np.complex) self.xm = self.xm.astype(np.complex) self.fxm = self.fxm.astype(np.complex) elif np.iscomplex(self.xm[0]): self.Gm = self.Gm.real self.xm = self.xm.real self.fxm = self.fxm.real self._converge_failures = 0 self._computed_jacobians = 0 # Execute guess_nonlinear if specified. system._guess_nonlinear() # When under a complex step from higher in the hierarchy, sometimes the step is too small # to trigger reconvergence, so nudge the outputs slightly so that we always get at least # one iteration of Broyden. if system.under_complex_step and self.options['cs_reconverge']: system._outputs._data += np.linalg.norm(system._outputs._data) * 1e-10 # Start with initial states. self.xm = self.get_vector(system._outputs) with Recording('Broyden', 0, self): self._solver_info.append_solver() # should call the subsystems solve before computing the first residual self._gs_iter() self._solver_info.pop() self._run_apply() norm = self._iter_get_norm() norm0 = norm if norm != 0.0 else 1.0 return norm0, norm def _iter_get_norm(self): """ Return the norm of only the residuals requested in options. Returns ------- float Norm of the residuals. """ # Need to cache the initial residuals, which is done in this function. self.fxm = fxm = self.get_vector(self._system()._residuals) if not self._full_inverse: # Use full model residual for driving the main loop convergence. fxm = self._system()._residuals._data return self.compute_norm(fxm)
[docs] def compute_norm(self, vec): """ Compute norm of the vector. Under MPI, compute the norm on rank 0, and broadcast it to all other ranks. Parameters ---------- vec : ndarray Array of real or complex values. For MPI on rank 0, should be full dimension of the openmdao vector with duplicate indices removed. Returns ------- float Norm of vec, computed on rank 0 and broadcast to all other ranks. """ return np.linalg.norm(vec)
def _single_iteration(self): """ Perform the operations in the iteration loop. """ system = self._system() Gm = self._update_inverse_jacobian() fxm = self.fxm delta_xm = -Gm.dot(fxm) if self.linesearch: self._solver_info.append_subsolver() self.set_states(self.xm) self.set_linear_vector(delta_xm) self.linesearch.solve() xm = self.get_vector(system._outputs) self._solver_info.pop() else: # Update the new states in the model. xm = self.xm + delta_xm self.set_states(xm) # Run the model. with Recording('Broyden', 0, self): self._solver_info.append_solver() self._gs_iter() self._solver_info.pop() self._run_apply() fxm1 = fxm.copy() self.fxm = fxm = self.get_vector(system._residuals) delta_fxm = fxm - fxm1 # States may have been further converged hierarchically. xm = self.get_vector(system._outputs) delta_xm = xm - self.xm # Determine whether to update Jacobian. self._recompute_jacobian = False opt = self.options if self._computed_jacobians <= opt['max_jacobians']: converge_ratio = self.compute_norm(fxm) / self.compute_norm(fxm1) if converge_ratio > opt['diverge_limit']: self._recompute_jacobian = True elif converge_ratio > opt['converge_limit']: self._converge_failures += 1 if self._converge_failures >= opt['max_converge_failures']: self._recompute_jacobian = True else: self._converge_failures = 0 # Cache for next iteration. self.delta_xm = delta_xm self.delta_fxm = delta_fxm self.fxm = fxm self.xm = xm self.Gm = Gm def _update_inverse_jacobian(self): """ Update the inverse Jacobian for a new Broyden iteration. Returns ------- ndarray Updated inverse Jacobian. """ Gm = self.Gm # Apply the Broyden Update approximation to the previous value of the inverse jacobian. if self.options['update_broyden'] and not self._recompute_jacobian: dfxm = self.delta_fxm fact = np.linalg.norm(dfxm) # Sometimes you can get stuck, particularly when enforcing bounds in a linesearch. # Make sure we don't update in this case because of divide by zero. if fact > self.options['atol']: Gm += np.outer((self.delta_xm - Gm.dot(dfxm)), dfxm * (1.0 / fact**2)) # Solve for total derivatives of user-requested residuals wrt states. elif self.options['compute_jacobian']: if self._full_inverse: Gm = self._compute_full_inverse_jacobian() else: Gm = self._compute_inverse_jacobian() self._computed_jacobians += 1 # Set inverse Jacobian to identity scaled by alpha. # This is the default starting point used by scipy and the general broyden algorithm. else: Gm = np.diag(np.full(self.size, -self.options['alpha'], dtype=Gm.dtype)) return Gm
[docs] def get_vector(self, vec): """ Return a vector containing the values of vec at the states specified in options. This is the full incoming vec when no states are defined. When under MPI, the values are appopriately gathered without duplicates to rank 0. Parameters ---------- vec : <Vector> Vector from which to extract state values. Returns ------- ndarray Array containing values of vector at desired states. """ if self._full_inverse: xm = vec._data.copy() else: states = self.options['state_vars'] xm = self.xm.copy() for name in states: i, j = self._idx[name] xm[i:j] = vec[name] return xm
[docs] def set_states(self, new_val): """ Set new values for states specified in options. Parameters ---------- new_val : ndarray New values for states. """ outputs = self._system()._outputs if self._full_inverse: outputs._data[:] = new_val else: states = self.options['state_vars'] for name in states: i, j = self._idx[name] outputs[name] = new_val[i:j]
[docs] def set_linear_vector(self, dx): """ Copy values from step into the linear vector for backtracking. Parameters ---------- dx : ndarray Full step in the states for this iteration. """ linear = self._system()._vectors['output']['linear'] if self._full_inverse: linear._data[:] = dx else: linear.set_const(0.0) for name in self.options['state_vars']: i, j = self._idx[name] linear[name] = dx[i:j]
def _compute_inverse_jacobian(self): """ Compute inverse Jacobian for system by doing a linear solve for each state. Returns ------- ndarray New inverse Jacobian. """ # TODO : Consider promoting this capability out into OpenMDAO so other solvers can use the # same code. # TODO : Can do each state in parallel if procs are available. system = self._system() states = self.options['state_vars'] d_res = system._vectors['residual']['linear'] d_out = system._vectors['output']['linear'] inv_jac = self.Gm d_res.set_const(0.0) # Disable local fd approx_status = system._owns_approx_jac system._owns_approx_jac = False # Linearize model. ln_solver = self.linear_solver do_sub_ln = ln_solver._linearize_children() my_asm_jac = ln_solver._assembled_jac system._linearize(my_asm_jac, sub_do_ln=do_sub_ln) if my_asm_jac is not None and system.linear_solver._assembled_jac is not my_asm_jac: my_asm_jac._update(system) self._linearize() for wrt_name in states: i_wrt, j_wrt = self._idx[wrt_name] if wrt_name in d_res: d_wrt = d_res[wrt_name] for j in range(j_wrt - i_wrt): # Increment each variable. if wrt_name in d_res: d_wrt[j] = 1.0 # Solve for total derivatives. ln_solver.solve(['linear'], 'fwd') # Extract results. for of_name in states: i_of, j_of = self._idx[of_name] inv_jac[i_of:j_of, i_wrt + j] = d_out[of_name] if wrt_name in d_res: d_wrt[j] = 0.0 # Enable local fd system._owns_approx_jac = approx_status return inv_jac def _compute_full_inverse_jacobian(self): """ Compute inverse Jacobian for entire system vector. Only the DirectSolver is supported here. Returns ------- ndarray New inverse Jacobian. """ system = self._system() # Disable local fd approx_status = system._owns_approx_jac system._owns_approx_jac = False # Linearize model. ln_solver = self.linear_solver do_sub_ln = ln_solver._linearize_children() my_asm_jac = ln_solver._assembled_jac system._linearize(my_asm_jac, sub_do_ln=do_sub_ln) if my_asm_jac is not None and system.linear_solver._assembled_jac is not my_asm_jac: my_asm_jac._update(system) inv_jac = self.linear_solver._inverse() # Enable local fd system._owns_approx_jac = approx_status return inv_jac def _mpi_print_header(self): """ Print header text before solving. """ if self.options['iprint'] > 0 and self._system().comm.rank == 0: pathname = self._system().pathname if pathname: nchar = len(pathname) prefix = self._solver_info.prefix header = prefix + "\n" header += prefix + nchar * "=" + "\n" header += prefix + pathname + "\n" header += prefix + nchar * "=" print(header)
[docs] def cleanup(self): """ Clean up resources prior to exit. """ super(BroydenSolver, self).cleanup() if self.linear_solver: self.linear_solver.cleanup() if self.linesearch: self.linesearch.cleanup()