The preprocessor now writes all the symmetric elements in the "hp"
matrix (derivatives of the hessian w.r.t. parameters), for consistency with all
other derivatives output.
Previously it was only writing one of the two symmetric elements, when indices
of endogenous were different.
Also, no longer compute two times symmetric elements in derivation w.r.t.
parameters at order 2, for consistency with derivation w.r.t. endogenous.
It is therefore now necessary to duplicate them in the output to keep behavior
unchanged.
When creating the sparse matrix (in MATLAB or C mode), since storage is in
column-major order, output the first column, then the second, then the third.
This gives a significant performance boost in use_dll mode (at both compilation
and runtime), because it facilitates memory accesses and expression reusage.
New options "mexext" and "matlabroot" are introduced, so that the preprocessor
knows where to find MATLAB and which architecture to compile for.
Only recent gcc is now supported. A set of optimization flags is used so that
compilation goes reasonably fast on large models.
Consequently, options "msvc", "mingw" and "cygwin" have been removed.
In particular, it is necessary to turn back DataTree::AddVariable() into a
non-virtual method, because it is called from DataTree's constructor. Enforcing
the absence of leads/lags is now done using a new boolean DataTree::is_static.
Take advantage of the new copy constructor for handling
PlannerObjectiveStatement more elegantly.
Unfortunately it is not possible to implement *move* constructor / assigment
operators, because the reference ExprNode::datatree is not mutable.
This facilitates switching variable types on the fly. In particular, this
allows removing the hack in DynamicModel::updateAfterVariableChange() that way
basically recreating all the nodes after the type change.
This mimicks the structure of M-functions (though the logic for filling the
temporary terms vector is a bit different).
This change implied a modification in the way we compute the checksum in case
of block decomposition (the temporary terms for the C output are not correctly
computed in that case).
Given a previously declared var_model, the var_expectation_model statement is
used to declare a way of forming expectations with this VAR (possibly using a
finite or infinite discounted sum). The var_expectation operator now takes a
single argument, the name of the var_expectation_model.
For the moment, this only works when the var_model is using equations
explicitly declared in the model block.