This section provides an overview of using and configuring solvers for IDAES. In general, standard Pyomo solver interfaces and features are used in IDAES, but IDAES provides a few extensions to make working with solvers slightly easier. Some IDAES solver features are documented in other sections, so references are provided as appropriate.
The global solver settings can be set via the IDAES configuration system<reference_guides/configuration:Configuration>. This feature is handy in IDAES where multiple solver objects are used for initialization before finally solving a problem. Since IDAES default solver settings differ from Pyomo, users must explicitly enable the IDAES solver configuration system with the use_idaes_solver_configuration_defaults() function.
idaes.core.solvers.config.use_idaes_solver_configuration_defaults
Typically users can use the standard Pyomo SoverFactory to get a solver. If a solver is needed in a general model or utility, a utility function <reference_guides/core/util/misc:get_solver> (idaes.core.util.misc.get_solver) provides a default or user configured solver at runtime. This is used by IDAES core models and tests.
A logger for solver-related log messages can be obtained from the idaes.logger.getSolveLogger() function (documented here<reference_guides/logging:idaes.solve Loggers>). IDAES also has features for redirecting solver output to a log (see Logging Solver Output<reference_guides/logging:Logging Solver Output>).
There are some functions available to check what features are available to solvers and to help with basic solver testing.
idaes.core.solvers.ipopt_has_linear_solver
IDAES provides an AMPL solver interface for the PETSc solver suite, (see the PETSc website). PETSc provides nonlinear equation (NLE) and differential algebraic equation (DAE) solvers. Both NLE and DAE solvers are capable of solving simulation problems with zero degrees of freedom. These solvers may be useful for initial model development, initialization, and running simulation cases without optimization.
PETSc includes optimization solvers, but they are not currently supported by the IDAES AMPL solver wrapper. Optimization support will likely be added in the future.
For the following discussion regarding the PETSc solver interface, the following terminology is used.
- Derivative variable: a time derivative
- Differential variable: a variable that is differentiated with respect to time
- Algebraic variable: a variable with no explicit time derivative appearing in the problem
- State variables: the set of algebraic and differential variables
- Time variable: a variable representing time
DAE problems do not need to include a time variable, but, if they do, there can only be one. Differential variables do not need to explicitly appear in constraints, but their time derivatives do. DAE problems must have zero degrees of freedom, which means the number of constraints must equal the number of state variables.
The PETSc solver is an extra binary package, and not installed by default. If you are using a supported Linux distribution, you can use the command idaes get-extensions --extra petsc to install it.
There is no precompiled PETSc solver for Windows, but here are two options for Windows installation. The easiest option is to run the available precompiled Linux version via the WSL (see binary installation <tutorials/getting_started/binaries:Using the WSL> for details). Expert users may wish to compile their own solver. Source code is available in the idaes-ext repo. If you can compile PETSc for Windows, compiling the interface is trivial (see PETSc's windows installation documentation).
The IDAES PETSc package also includes Python modules for reading binary data written by the PETSc solver. On Windows, some manual installation of the Python modules is required. If you are using the WSL method to run PETSc, copy the petscpy directory from the Linux package you are using to the IDAES binary directory. You can find the IDAES binary directory by running the command idaes bin-directory in the OS command shell (e.g. Bash, Windows CMD, PowerShell). If IDAES is installed in a Python environment, the environment must be active. The primary use for these Python modules is to read trajectory files saved by the TS solver.
Importing idaes.core.solvers.petsc registers two new solvers "petsc_snes" and "petsc_ts." The "petsc_snes" solver provides nonlinear equation solvers. The SNES (Scalable Nonlinear Equation Solvers) solvers are strictly nonlinear equations solvers, so they cannot directly handle optimization problems and the problem must have zero degrees of freedom. The TS (time-stepping) solvers require specialized suffixes to designate derivative, differential, algebraic, and time variables and the associations between derivative and differential variables. Both the SNES and TS solvers accept the standard scaling factor suffixes, but for TS solvers, derivatives and differential variables cannot be scaled independently, so the differential variable scale is used. Currently, time cannot be scaled for TS solvers.
Standard PETSc command line options are available to the solvers except that for compatibility reasons, they are specified with a double dash instead of single. Command line options can be used to set up the SNES and TS solvers. Currently only implicit TS solvers are supported. Commonly used TS types are:
- "beuler", implicit Euler,
- "cn", Crank-Nicolson, and
- "alpha", generalized-alpha method.
To get started, important command line options are for SNES solvers are described https://petsc.org/release/docs/manualpages/SNES/SNESSetFromOptions.html and TS options are described https://petsc.org/release/docs/manualpages/TS/TSSetFromOptions.html. Remember that options specified through the IDAES AMPL interface use a double dash rather than the single dash shown in the PETSc documentation. Users can also set linear solver and preconditioner options, and are encouraged to read the PETSc documentation if needed.
The easiest way to use the "petsc_ts" solver is to use the utility method that converts a standard Pyomo.DAE to the form used by the solver.
The utility for solving Pyomo.DAE problems uses the PETSc TS solvers to integrate between each time point in the Pyomo.DAE discretization. The results are stored for each time point in the Pyomo model. This can be used to initialize and verify the results of the full time-discretized model. For example this could be used to determine if the time steps used in the discretization are too big by comparing the integrator solution to the fully discretized model solution.
To quickly run a DAE model, a time discretization with one element can be made. In this case, the PETSc TS solver will integrate from the initial condition to the end point. Results for intermediate times can be read from PETSc's stored trajectory data if the proper solver options are specified.
Although it is probably not typical of Pyomo.DAE models, a time variable can be specified. Constraints can be written as explicit functions of time. For example, some model input could be ramped up or down as a function of time.
The integrator approach does not support some constraints that can be solved using the full discretized model. For example, you can have constraints to calculate initial conditions, but cannot have constraints that specify final or intermediate conditions. Optimization is not directly possible, but future implementation of optimization solvers in combination with adjoint sensitivity calculations may enable optimization.
Non-time-indexed variables and constraints should usually be solved with the initial conditions in the first step. Non-time-indexed variables can optionally be detected and added to the equations solved for the initial conditions, or explicitly specified by the user. Users will have to take care not to include non-time indexed constraints that contain time-indexed variables at times other than the initial time. If such constraints exist for the fully discretized model users should deactivate them as appropriate.
The following function can be used to solve the DAE.
idaes.core.solvers.petsc.petsc_dae_by_time_element
Usually if you want to read the trajectory data from the solver, you will want to solve the whole time domain at once, so you will want to specify one time element in the Pyomo.DAE discretization. By specifying the --ts_save_trajectory=1 and --ts_trajectory_type=visualization options the trajectory information will be saved. Supplying the vars_stub argument to the petsc_dae_by_time_element() function will copy the *.col and *.typ files needed to interpret the trajectory data to the current working directory.
The PetscTrajectory class has methods to read in trajectory data and interpolate time points as needed.
idaes.core.solvers.petsc.PetscTrajectory
Most IDAES models use Pyomo.DAE and that is probably the easiest way to set up a DAE problem, however you may directly construct a DAE problem.
There are two suffixes that need to be specified to use the PETSc TS solvers from Pyomo. The first is an integer suffix dae_suffix, which specifies the variable types. The algebraic variables do not need to be included, but 0 specifies algebraic variables, 1 specifies differential variables, 2 specifies derivative variables, and 3 specifies the time variable. A variable for time is optional, and only one time variable can be specified. The other suffix is an integer suffix dae_link which contains differential and derivative variables. The integer in the suffix links the derivative to it's differential variable, by specifying an integer greater than 0 that is unique to the pair.
If there are differential variables that do not appear in the constraints, they can be supplied to the export_nonlinear_variables argument of solve. For the trajectory data, you will also want to use symbolic_solver_labels.
To solve the problem, start with the initial conditions in the Pyomo model. After the solve the final conditions will be in the Pyomo model. To get intermediate results, you will need to store the solver trajectory as described previously.
This section provides PETSc features that are planned to be supported in the future, but are not currently supported.
- Enable parallel methods
- Enable IMEX methods for TS solvers
- Enable TAO optimization solvers
- Provide PETSc Python functions for reading trajectory data (rather than requiring users to get them manually).
The idaes.core.solvers.features module provides functions to return simple models of various types. These models can be used to test if solvers are available and functioning properly. They can also be used to test that various optional solver features are available. These functions all return a tuple where the first element is a model of the specified type, and the remaining elements are the correct solved values for select variables.
idaes.core.solvers.features.lp
idaes.core.solvers.features.milp
idaes.core.solvers.features.nlp
idaes.core.solvers.features.minlp
idaes.core.solvers.features.nle
idaes.core.solvers.features.dae