By the way, remove the BlockType stuff which was purely informative (and it’s
not worth carrying over prologue and epilogue information just for that).
Previously, the cutoff option would also impact the block decomposition itself,
since it would had an influence on the incidence matrix used for computing the
blocks and their derivatives.
The problem is that, in the general case, it’s quite possible that an element
of the numerical Jacobian be zero at the evaluation point, while being quite
different from zero along the simulation path. A typical example is an
expression of the type x*y, where y is an endogenous and x is an exogenous not
present in the initval block (and hence initialized to zero).
It has been superseded by ModelTree::blocks_derivatives.
By the way, fix the initial number of non-zero elements in sparse Jacobian.
Also avoid computing suboptimal temporary terms.
— use a std::map for storing block derivatives
— remove redundant ModelTree::first_chain_rule_derivatives structure
— remove unused codepaths in StaticModel
— DynamicModel: simplify code that determines the type of derivatives in a
block. We now use a slightly different categorization.
— by the way, fix the max lead/lag information for blocks that are obtained via
merging. A workaround was previously implemented in
DynamicModel::get_Derivative(), but it is no longer needed with this fix.
This was only adding unneeded complexity, for no clear reason (we’re very far
from reaching 2³¹ equations, and if we wanted to support models that large, it
would be better to use long integers to avoid being limited to 2³²).
— return output arguments on the left-hand side
— do not pass class members as input/output arguments
By the way, fix a (benign) vector allocation bug in
{Static,Dynamic}Model::computeChainRuleJacobian().
Backward incompatible change: the temporary terms for 3rd order are now stored
in "temporary_terms_third_derivative" (without the final "s"; same for external
functions), for consistency with the name of the slot for the derivatives
themselves ("third_derivative").
Ref dynare#217
Does not work for Julia mode, neither with block and bytecode.
Note: in DLL mode, the number of temporary terms in no longer given in the
‘ntt’ symbol; it must be computed using ‘M_.dynamic_tmp_nbr’.
Ref dynare#217
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.