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solver_handler

SolverHandler

Creates and runs a solver instance.

This may be an optimiser (e.g. VMCON) or an equation solver (e.g. fsolve).

Parameters:

Name Type Description Default
models Models

physics and engineering model objects

 required
solver_name str

which solver to use, as specified in solver.py

 required
Source code in process/core/solver/solver_handler.py 
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class SolverHandler:
    """Creates and runs a solver instance.

    This may be an optimiser (e.g. VMCON) or an equation solver (e.g. fsolve).

    Parameters
    ----------
    models : process.main.Models
        physics and engineering model objects
    solver_name : str
        which solver to use, as specified in solver.py
    """

    def __init__(self, models, solver_name):
        self.models = models
        self.solver_name = solver_name

    def run(self):
        """Run solver and retry if it fails in certain ways."""
        # Initialise iteration variables and bounds in Fortran
        load_iteration_variables()
        load_scaled_bounds()

        # Initialise iteration variables and bounds in Python: relies on Fortran
        # iteration variables being defined above
        # Trim maximum size arrays down to actually used size
        n = numerics.nvar
        x = numerics.xcm[:n]
        bndl = numerics.itv_scaled_lower_bounds[:n]
        bndu = numerics.itv_scaled_upper_bounds[:n]

        # Define total number of constraints and equality constraints
        m = numerics.neqns + numerics.nineqns
        meq = numerics.neqns

        # Evaluators() calculates the objective and constraint functions and
        # their gradients for a given vector x
        evaluators = Evaluators(self.models, x)

        # Configure solver for problem
        self.solver = get_solver(self.solver_name)
        self.solver.set_evaluators(evaluators)
        self.solver.set_bounds(bndl, bndu)
        self.solver.set_opt_params(x)
        self.solver.set_constraints(m, meq)
        ifail = self.solver.solve()

        # If VMCON optimisation has failed then try altering value of epsfcn
        if self.solver_name == "vmcon":
            if ifail != 1:
                print("Trying again with new epsfcn")
                # epsfcn is only used in evaluators.Evaluators()
                # TODO epsfcn could be set in Evaluators instance now, don't need to
                # set/unset in numerics module
                numerics.epsfcn = numerics.epsfcn * 10  # try new larger value
                print("new epsfcn = ", numerics.epsfcn)

                ifail = self.solver.solve()
                # First solution attempt failed (ifail != 1): supply ifail value
                # to next attempt
                numerics.epsfcn = numerics.epsfcn / 10  # reset value

            if ifail != 1:
                print("Trying again with new epsfcn")
                numerics.epsfcn = numerics.epsfcn / 10  # try new smaller value
                print("new epsfcn = ", numerics.epsfcn)
                ifail = self.solver.solve()
                numerics.epsfcn = numerics.epsfcn * 10  # reset value

            # If VMCON has exited with error code 5 try another run using a multiple
            # of the identity matrix as input for the Hessian b(n,n)
            # Only do this if VMCON has not iterated (nviter=1)
            if ifail == 5 and numerics.nviter < 2:
                print(
                    "VMCON error code = 5.  Rerunning VMCON with a new initial "
                    "estimate of the second derivative matrix."
                )
                self.solver.set_b(2.0)
                ifail = self.solver.solve()

        self.output()

        return ifail

    def output(self):
        """Store results back in Fortran numerics module.

        Objective function value, solution vector and constraints vector.
        """
        numerics.norm_objf = self.solver.objf
        # Slicing required due to Fortran arrays being maximum possible, rather
        # than required, size
        numerics.xcm[: self.solver.x.shape[0]] = self.solver.x
        numerics.rcm[: self.solver.conf.shape[0]] = self.solver.conf

models = models instance-attribute

solver_name = solver_name instance-attribute

run()

Run solver and retry if it fails in certain ways.

Source code in process/core/solver/solver_handler.py 
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def run(self):
    """Run solver and retry if it fails in certain ways."""
    # Initialise iteration variables and bounds in Fortran
    load_iteration_variables()
    load_scaled_bounds()

    # Initialise iteration variables and bounds in Python: relies on Fortran
    # iteration variables being defined above
    # Trim maximum size arrays down to actually used size
    n = numerics.nvar
    x = numerics.xcm[:n]
    bndl = numerics.itv_scaled_lower_bounds[:n]
    bndu = numerics.itv_scaled_upper_bounds[:n]

    # Define total number of constraints and equality constraints
    m = numerics.neqns + numerics.nineqns
    meq = numerics.neqns

    # Evaluators() calculates the objective and constraint functions and
    # their gradients for a given vector x
    evaluators = Evaluators(self.models, x)

    # Configure solver for problem
    self.solver = get_solver(self.solver_name)
    self.solver.set_evaluators(evaluators)
    self.solver.set_bounds(bndl, bndu)
    self.solver.set_opt_params(x)
    self.solver.set_constraints(m, meq)
    ifail = self.solver.solve()

    # If VMCON optimisation has failed then try altering value of epsfcn
    if self.solver_name == "vmcon":
        if ifail != 1:
            print("Trying again with new epsfcn")
            # epsfcn is only used in evaluators.Evaluators()
            # TODO epsfcn could be set in Evaluators instance now, don't need to
            # set/unset in numerics module
            numerics.epsfcn = numerics.epsfcn * 10  # try new larger value
            print("new epsfcn = ", numerics.epsfcn)

            ifail = self.solver.solve()
            # First solution attempt failed (ifail != 1): supply ifail value
            # to next attempt
            numerics.epsfcn = numerics.epsfcn / 10  # reset value

        if ifail != 1:
            print("Trying again with new epsfcn")
            numerics.epsfcn = numerics.epsfcn / 10  # try new smaller value
            print("new epsfcn = ", numerics.epsfcn)
            ifail = self.solver.solve()
            numerics.epsfcn = numerics.epsfcn * 10  # reset value

        # If VMCON has exited with error code 5 try another run using a multiple
        # of the identity matrix as input for the Hessian b(n,n)
        # Only do this if VMCON has not iterated (nviter=1)
        if ifail == 5 and numerics.nviter < 2:
            print(
                "VMCON error code = 5.  Rerunning VMCON with a new initial "
                "estimate of the second derivative matrix."
            )
            self.solver.set_b(2.0)
            ifail = self.solver.solve()

    self.output()

    return ifail

output()

Store results back in Fortran numerics module.

Objective function value, solution vector and constraints vector.

Source code in process/core/solver/solver_handler.py 
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def output(self):
    """Store results back in Fortran numerics module.

    Objective function value, solution vector and constraints vector.
    """
    numerics.norm_objf = self.solver.objf
    # Slicing required due to Fortran arrays being maximum possible, rather
    # than required, size
    numerics.xcm[: self.solver.x.shape[0]] = self.solver.x
    numerics.rcm[: self.solver.conf.shape[0]] = self.solver.conf