– new option “endval_steady” to pf_setup command to recompute terminal
steady state in the homotopy loop
– new options “homotopy_linearization_fallback” and
“homotopy_marginal_linearization_fallback” to pf_solver and pfwee_solver
commands, to get an approximate solution when homotopy fails to go to 100%
– new options “homotopy_initial_step_size”, “homotopy_min_step_size”,
“homotopy_step_size_increase_success_count” and “homotopy_max_completion_share”
to pf_solver and pfwee_solver commands to fine tune the homotopy behaviour
– removed option “homotopy_alt_starting_point” to pf_solver command, not really
useful
– new options “steady_solve_algo”, “steady_tolf”, “steady_tolx”,
“steady_maxit”, “steady_markowitz” to pf_solver and pfwee_solver commands, to
control the computation of the terminal steady state (and remove the
equivalent options which previously had different names in pfwee_solver command)
– Remove the terminal_steady_state_as_guess_value option to pfwee_solver
– pfwee_setup now sets the same guess values as pf_setup (i.e. terminal steady
state at all periods)
– With constant_simulation_length option, pfwee_solver uses terminal steady
state as guess values for periods that are added to the simulation
Explain that the solver is a direct sparse LU, to differentiate it from
stack_solve_algo={2,3,4} which use the same Newton algorithm but with different
solvers.
Use the new time-recursive block decomposition computed by the preprocessor
for:
- the simulation of backward models with “simul_backward”
- the perfect foresight simulation of purely backward/forward/static models
Also note that in this case, the preprocessor now defaults to “mfs=3” (i.e. it
minimizes the set of feedback variables and tries to renormalize equations).
This replaces the previous algorithm based on Dulmage-Mendelsohn (dmperm), plus
an ad hoc identification of some equations that can be evaluated (those with a
LHS equal to a variable, the log of a variable, or the diff-log of a variable).
By the way, the block_trust_region MEX has been modified so that it accepts a
boolean argument to decide whether it performs a Dulmage-Mendelsohn
decomposition (if not, then it performs a simple trust region on the whole
nonlinear system).
This provides a significant performance improvement (of almost an order of
magnitude for solve_algo=14 on a 700 equations model).
Sébastien Villemot
Johannes Pfeifer
Willi Mutschler
Stéphane Adjemian (Guts)