LinearBlockJac

LinearBlockJac uses the block Jacobi method to solve the linear system. The method is similar to that used by the LinearBlockGS solver, except that it propagates the derivatives from outputs to inputs only once per iteration. When to choose this solver over the other ones is an advanced topic.

LinearBlockJac is a block solver, so you can specify different linear solvers in the subsystems and they will be utilized to solve the subsystem linear problem.

Here, we calculate the total derivatives across the Sellar system.

SellarDis1withDerivatives class definition

class SellarDis1withDerivatives(SellarDis1):
    """
    Component containing Discipline 1 -- derivatives version.
    """

    def setup_partials(self):
        # Analytic Derivs
        self.declare_partials(of='*', wrt='*')

    def compute_partials(self, inputs, partials):
        """
        Jacobian for Sellar discipline 1.
        """
        partials['y1', 'y2'] = -0.2
        partials['y1', 'z'] = np.array([[2.0 * inputs['z'][0], 1.0]])
        partials['y1', 'x'] = 1.0

SellarDis2withDerivatives class definition

class SellarDis2withDerivatives(SellarDis2):
    """
    Component containing Discipline 2 -- derivatives version.
    """

    def setup_partials(self):
        # Analytic Derivs
        self.declare_partials(of='*', wrt='*')

    def compute_partials(self, inputs, J):
        """
        Jacobian for Sellar discipline 2.
        """
        y1 = inputs['y1']
        if y1.real < 0.0:
            y1 *= -1
        if y1.real < 1e-8:
            y1 = 1e-8

        J['y2', 'y1'] = .5*y1**-.5
        J['y2', 'z'] = np.array([[1.0, 1.0]])

import numpy as np
import openmdao.api as om

from openmdao.test_suite.components.sellar import SellarDis1withDerivatives, SellarDis2withDerivatives

prob = om.Problem()
model = prob.model

model.add_subsystem('d1', SellarDis1withDerivatives(), promotes=['x', 'z', 'y1', 'y2'])
model.add_subsystem('d2', SellarDis2withDerivatives(), promotes=['z', 'y1', 'y2'])

model.add_subsystem('obj_cmp', om.ExecComp('obj = x**2 + z[1] + y1 + exp(-y2)',
                                           z=np.array([0.0, 0.0]), x=0.0),
                    promotes=['obj', 'x', 'z', 'y1', 'y2'])

model.add_subsystem('con_cmp1', om.ExecComp('con1 = 3.16 - y1'), promotes=['con1', 'y1'])
model.add_subsystem('con_cmp2', om.ExecComp('con2 = y2 - 24.0'), promotes=['con2', 'y2'])

model.nonlinear_solver = om.NonlinearBlockGS()
model.linear_solver = om.LinearBlockJac()

prob.setup()

prob.set_val('x', 1.)
prob.set_val('z', np.array([5.0, 2.0]))

prob.run_model()

wrt = ['z']
of = ['obj']

J = prob.compute_totals(of=of, wrt=wrt, return_format='flat_dict')

NL: NLBGS Converged in 8 iterations
LN: LNBJSolver 'LN: LNBJ' on system '' failed to converge in 10 iterations.

print(J['obj', 'z'][0][0])
print(J['obj', 'z'][0][1])

9.610009089277705
1.7844852158189335

LinearBlockJac Options

Option	Default	Acceptable Values	Acceptable Types	Description
assemble_jac	False	[True, False]	['bool']	Activates use of assembled jacobian by this solver.
atol	1e-10	N/A	N/A	absolute error tolerance
err_on_non_converge	False	[True, False]	['bool']	When True, AnalysisError will be raised if we don't converge.
iprint	1	N/A	['int']	whether to print output
maxiter	10	N/A	['int']	maximum number of iterations
rtol	1e-10	N/A	N/A	relative error tolerance

LinearBlockJac Constructor

The call signature for the LinearBlockJac constructor is:

LinearBlockJac.__init__(**kwargs)

Initialize attributes.

LinearBlockJac Option Examples

maxiter

This lets you specify the maximum number of Gauss-Seidel iterations to apply. In this example, we cut it back from the default, ten, down to five, so that it terminates a few iterations earlier and doesn’t reach the specified absolute or relative tolerance. Note that due to the delayed transfer of information, this takes more iterations to converge than the LinearBlockGS solver.

from openmdao.test_suite.components.sellar import SellarDis1withDerivatives, SellarDis2withDerivatives

prob = om.Problem()
model = prob.model

model.add_subsystem('d1', SellarDis1withDerivatives(), promotes=['x', 'z', 'y1', 'y2'])
model.add_subsystem('d2', SellarDis2withDerivatives(), promotes=['z', 'y1', 'y2'])

model.add_subsystem('obj_cmp', om.ExecComp('obj = x**2 + z[1] + y1 + exp(-y2)',
                                           z=np.array([0.0, 0.0]), x=0.0),
                    promotes=['obj', 'x', 'z', 'y1', 'y2'])

model.add_subsystem('con_cmp1', om.ExecComp('con1 = 3.16 - y1'), promotes=['con1', 'y1'])
model.add_subsystem('con_cmp2', om.ExecComp('con2 = y2 - 24.0'), promotes=['con2', 'y2'])

model.nonlinear_solver = om.NonlinearBlockGS()

model.linear_solver = om.LinearBlockJac()
model.linear_solver.options['maxiter'] = 5

prob.setup()

prob.set_val('x', 1.)
prob.set_val('z', np.array([5.0, 2.0]))

prob.run_model()

wrt = ['z']
of = ['obj']

J = prob.compute_totals(of=of, wrt=wrt, return_format='flat_dict')

NL: NLBGS Converged in 8 iterations
LN: LNBJSolver 'LN: LNBJ' on system '' failed to converge in 5 iterations.

print(J['obj', 'z'][0][0])
print(J['obj', 'z'][0][1])

9.602301066797569
1.7802249941460035

atol

Here, we set the absolute tolerance to a looser value that will trigger an earlier termination. After each iteration, the norm of the linear residuals is calculated by calling apply_linear. If this norm value is lower than the absolute tolerance atol, the iteration will terminate.

from openmdao.test_suite.components.sellar import SellarDis1withDerivatives, SellarDis2withDerivatives

prob = om.Problem()
model = prob.model

model.add_subsystem('d1', SellarDis1withDerivatives(), promotes=['x', 'z', 'y1', 'y2'])
model.add_subsystem('d2', SellarDis2withDerivatives(), promotes=['z', 'y1', 'y2'])

model.add_subsystem('obj_cmp', om.ExecComp('obj = x**2 + z[1] + y1 + exp(-y2)',
                                           z=np.array([0.0, 0.0]), x=0.0),
                    promotes=['obj', 'x', 'z', 'y1', 'y2'])

model.add_subsystem('con_cmp1', om.ExecComp('con1 = 3.16 - y1'), promotes=['con1', 'y1'])
model.add_subsystem('con_cmp2', om.ExecComp('con2 = y2 - 24.0'), promotes=['con2', 'y2'])

model.nonlinear_solver = om.NonlinearBlockGS()

model.linear_solver = om.LinearBlockJac()
model.linear_solver.options['atol'] = 1.0e-3

prob.setup(mode='rev')

prob.set_val('x', 1.)
prob.set_val('z', np.array([5.0, 2.0]))

prob.run_model()

wrt = ['z']
of = ['obj']

J = prob.compute_totals(of=of, wrt=wrt, return_format='flat_dict')

NL: NLBGS Converged in 8 iterations
LN: LNBJ Converged in 7 iterations

print(J['obj', 'z'][0][0])
print(J['obj', 'z'][0][1])

9.610162963993043
1.784569557267524

rtol

Here, we set the relative tolerance to a looser value that will trigger an earlier termination. After each iteration, the norm of the linear residuals is calculated by calling apply_linear. If the ratio of the currently calculated norm to the initial residual norm is lower than the relative tolerance rtol, the iteration will terminate.

from openmdao.test_suite.components.sellar import SellarDis1withDerivatives, SellarDis2withDerivatives

prob = om.Problem()
model = prob.model

model.add_subsystem('d1', SellarDis1withDerivatives(), promotes=['x', 'z', 'y1', 'y2'])
model.add_subsystem('d2', SellarDis2withDerivatives(), promotes=['z', 'y1', 'y2'])

model.add_subsystem('obj_cmp', om.ExecComp('obj = x**2 + z[1] + y1 + exp(-y2)',
                                           z=np.array([0.0, 0.0]), x=0.0),
                    promotes=['obj', 'x', 'z', 'y1', 'y2'])

model.add_subsystem('con_cmp1', om.ExecComp('con1 = 3.16 - y1'), promotes=['con1', 'y1'])
model.add_subsystem('con_cmp2', om.ExecComp('con2 = y2 - 24.0'), promotes=['con2', 'y2'])

model.nonlinear_solver = om.NonlinearBlockGS()

model.linear_solver = om.LinearBlockJac()
model.linear_solver.options['rtol'] = 1.0e-3

prob.setup(mode='rev')

prob.set_val('x', 1.)
prob.set_val('z', np.array([5.0, 2.0]))

prob.run_model()

wrt = ['z']
of = ['obj']

J = prob.compute_totals(of=of, wrt=wrt, return_format='flat_dict')

NL: NLBGS Converged in 8 iterations
LN: LNBJ Converged in 7 iterations

print(J['obj', 'z'][0][0])
print(J['obj', 'z'][0][1])

9.610162963993043
1.784569557267524