Skip to content
GitLab
Explore
Sign in
Register
Primary navigation
Search or go to…
Project
D
dynare
Manage
Activity
Members
Labels
Plan
Issues
Issue boards
Milestones
Wiki
Code
Merge requests
Repository
Branches
Commits
Tags
Repository graph
Compare revisions
Snippets
Build
Pipelines
Jobs
Pipeline schedules
Artifacts
Deploy
Releases
Container registry
Model registry
Operate
Environments
Monitor
Incidents
Analyze
Value stream analytics
Contributor analytics
CI/CD analytics
Repository analytics
Model experiments
Help
Help
Support
GitLab documentation
Compare GitLab plans
GitLab community forum
Contribute to GitLab
Provide feedback
Keyboard shortcuts
?
Snippets
Groups
Projects
Show more breadcrumbs
Sébastien Villemot
dynare
Commits
59318e58
Verified
Commit
59318e58
authored
1 month ago
by
Sébastien Villemot
Browse files
Options
Downloads
Patches
Plain Diff
Manual: misc improvements to the description of sparse Gaussian elimination solver
parent
77af2e99
No related branches found
No related tags found
No related merge requests found
Changes
1
Show whitespace changes
Inline
Side-by-side
Showing
1 changed file
doc/manual/source/the-model-file.rst
+13
-7
13 additions, 7 deletions
doc/manual/source/the-model-file.rst
with
13 additions
and
7 deletions
doc/manual/source/the-model-file.rst
+
13
−
7
View file @
59318e58
...
@@ -2990,8 +2990,9 @@ Finding the steady state with Dynare nonlinear solver
...
@@ -2990,8 +2990,9 @@ Finding the steady state with Dynare nonlinear solver
``5``
``5``
Newton algorithm with a sparse Gaussian elimination (SPE)
Newton algorithm with a sparse Gaussian elimination (SPE)
solver at each iteration (requires ``bytecode`` option, see
solver at each iteration. This algorithm requires the
:ref:`model-decl`).
:opt:`bytecode` option. The :ref:`markowitz <steady_markowitz>`
option can be used to control the behaviour of the algorithm.
``6``
``6``
...
@@ -3804,8 +3805,11 @@ speed-up on large models.
...
@@ -3804,8 +3805,11 @@ speed-up on large models.
Use the Laffargue-Boucekkine-Juillard (LBJ) algorithm proposed
Use the Laffargue-Boucekkine-Juillard (LBJ) algorithm proposed
in *Juillard (1996)* on top of a sparse Gaussian elimination
in *Juillard (1996)* on top of a sparse Gaussian elimination
(SPE) solver. The latter takes advantage of the similarity of
(SPE) solver. The latter takes advantage of the similarity of
the Jacobian across periods when searching for the pivots
the Jacobian across periods when searching for the pivots. This
(requires ``bytecode`` option, see :ref:`model-decl`).
algorithm requires the :opt:`bytecode` option. The following
options can be used to control the behaviour of the algorithm:
:ref:`markowitz <dynamic_markowitz>`,
:opt:`minimal_solving_periods <minimal_solving_periods = INTEGER>`.
``6``
``6``
...
@@ -3928,11 +3932,13 @@ speed-up on large models.
...
@@ -3928,11 +3932,13 @@ speed-up on large models.
procedure, *i.e.* which must be kept at their value corresponding to
procedure, *i.e.* which must be kept at their value corresponding to
100% of the shock during all homotopy iterations.
100% of the shock during all homotopy iterations.
.. _dynamic_markowitz:
.. option:: markowitz = DOUBLE
.. option:: markowitz = DOUBLE
Value of the Markowitz criterion, used to select the
Value of the Markowitz criterion, used to select the
pivot (see
pivot. Only used when ``stack_solve_algo = 5``. Default:
:ref:`markowitz <steady_markowitz>` for more details). Only used when
``0.5``.
``stack_solve_algo = 5``. Default:
``0.5``.
.. option:: minimal_solving_periods = INTEGER
.. option:: minimal_solving_periods = INTEGER
...
...
This diff is collapsed.
Click to expand it.
Preview
0%
Loading
Try again
or
attach a new file
.
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Save comment
Cancel
Please
register
or
sign in
to comment