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DynamicModel.hh
BlockTriangular.cc 37.78 KiB
/*
* Copyright (C) 2007-2008 Dynare Team
*
* This file is part of Dynare.
*
* Dynare is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* Dynare is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with Dynare. If not, see <http://www.gnu.org/licenses/>.
*/
// TODO Apply Block Decomposition to the static model
#include <iostream>
#include <sstream>
#include <fstream>
#include <ctime>
#include <cstdlib>
#include <cstring>
#include <cmath>
using namespace std;
//------------------------------------------------------------------------------
#include "BlockTriangular.hh"
//------------------------------------------------------------------------------
BlockTriangular::BlockTriangular(const SymbolTable &symbol_table_arg) :
symbol_table(symbol_table_arg),
normalization(symbol_table_arg)
{
bt_verbose = 0;
ModelBlock = NULL;
Model_Max_Lead = 0;
Model_Max_Lag = 0;
periods = 0;
}
//------------------------------------------------------------------------------
//For a lead or a lag build the Incidence Matrix structures
List_IM*
BlockTriangular::Build_IM(int lead_lag)
{
List_IM* pIM = new List_IM;
int i;
Last_IM->pNext = pIM;
pIM->IM = (bool*)malloc(endo_nbr * endo_nbr * sizeof(pIM->IM[0]));
for(i = 0;i < endo_nbr*endo_nbr;i++)
pIM->IM[i] = 0;
pIM->lead_lag = lead_lag;
if(lead_lag > 0)
{
if(lead_lag > Model_Max_Lead)
Model_Max_Lead = lead_lag;
}
else
{
if( -lead_lag > Model_Max_Lag)
Model_Max_Lag = -lead_lag;
}
pIM->pNext = NULL;
Last_IM = pIM;
return (pIM);
}
//------------------------------------------------------------------------------
// initialize all the incidence matrix structures
void
BlockTriangular::init_incidence_matrix(int nb_endo)
{
int i;
endo_nbr = nb_endo;
First_IM = new List_IM;
First_IM->IM = (bool*)malloc(nb_endo * nb_endo * sizeof(First_IM->IM[0]));
for(i = 0;i < nb_endo*nb_endo;i++)
First_IM->IM[i] = 0;
First_IM->lead_lag = 0;
First_IM->pNext = NULL;
Last_IM = First_IM;
//cout << "init_incidence_matrix done \n";
}
void
BlockTriangular::Free_IM(List_IM* First_IM) const
{
#ifdef DEBUG
cout << "Free_IM\n";
#endif
List_IM *Cur_IM, *SFirst_IM;
Cur_IM = SFirst_IM = First_IM;
while(Cur_IM)
{
First_IM = Cur_IM->pNext;
free(Cur_IM->IM);
delete Cur_IM;
Cur_IM = First_IM;
}
//free(SFirst_IM);
//delete SFirst_IM;
}
//------------------------------------------------------------------------------
// Return the inceidence matrix related to a lead or a lag
List_IM*
BlockTriangular::Get_IM(int lead_lag)
{
List_IM* Cur_IM;
Cur_IM = First_IM;
while ((Cur_IM != NULL) && (Cur_IM->lead_lag != lead_lag))
Cur_IM = Cur_IM->pNext;
return (Cur_IM);
}
bool*
BlockTriangular::bGet_IM(int lead_lag) const
{
List_IM* Cur_IM;
Cur_IM = First_IM;
while ((Cur_IM != NULL) && (Cur_IM->lead_lag != lead_lag))
{
Cur_IM = Cur_IM->pNext;
}
if((Cur_IM->lead_lag != lead_lag) || (Cur_IM==NULL))
{
cout << "the incidence matrix with lag " << lead_lag << " does not exist !!";
exit(-1);
}
return (Cur_IM->IM);
}
//------------------------------------------------------------------------------
// Fill the incidence matrix related to a lead or a lagvoid
BlockTriangular::fill_IM(int equation, int variable_endo, int lead_lag)
{
List_IM* Cur_IM;
//cout << "equation=" << equation << " variable_endo=" << variable_endo << " lead_lag=" << lead_lag << "\n";
Cur_IM = Get_IM(lead_lag);
if(equation >= endo_nbr)
{
cout << "Error : The model has more equations (at least " << equation + 1 << ") than declared endogenous variables (" << endo_nbr << ")\n";
system("PAUSE");
exit( -1);
}
if (!Cur_IM)
Cur_IM = Build_IM(lead_lag);
Cur_IM->IM[equation*endo_nbr + variable_endo] = 1;
}
//------------------------------------------------------------------------------
// unFill the incidence matrix related to a lead or a lag
void
BlockTriangular::unfill_IM(int equation, int variable_endo, int lead_lag)
{
List_IM* Cur_IM;
//cout << "lead_lag=" << lead_lag << "\n";
Cur_IM = Get_IM(lead_lag);
/*if(equation >= endo_nbr)
{
cout << "Error : The model has more equations (at least " << equation + 1 << ") than declared endogenous variables (" << endo_nbr << ")\n";
system("PAUSE");
exit( -1);
}*/
if (!Cur_IM)
Cur_IM = Build_IM(lead_lag);
Cur_IM->IM[equation*endo_nbr + variable_endo] = 0;
/*system("pause");*/
}
//------------------------------------------------------------------------------
//Print azn incidence matrix
void
BlockTriangular::Print_SIM(bool* IM, int n) const
{
int i, j;
for(i = 0;i < n;i++)
{
cout << " ";
for(j = 0;j < n;j++)
cout << IM[i*n + j] << " ";
cout << "\n";
}
}
//------------------------------------------------------------------------------
//Print all incidence matrix
void
BlockTriangular::Print_IM(int n) const
{
List_IM* Cur_IM;
Cur_IM = First_IM;
cout << "-------------------------------------------------------------------\n";
while(Cur_IM)
{
cout << "Incidence matrix for lead_lag = " << Cur_IM->lead_lag << "\n";
Print_SIM(Cur_IM->IM, n);
Cur_IM = Cur_IM->pNext;
}
}
//------------------------------------------------------------------------------
// Swap rows and columns of the incidence matrix
void
BlockTriangular::swap_IM_c(bool *SIM, int pos1, int pos2, int pos3, simple* Index_Var_IM, simple* Index_Equ_IM, int n)
{
int tmp_i, j;
bool tmp_b;
/* We exchange equation (row)...*/
if(pos1 != pos2)
{
tmp_i = Index_Equ_IM[pos1].index;
Index_Equ_IM[pos1].index = Index_Equ_IM[pos2].index;
Index_Equ_IM[pos2].index = tmp_i;
for(j = 0;j < n;j++)
{
tmp_b = SIM[pos1 * n + j];
SIM[pos1*n + j] = SIM[pos2 * n + j];
SIM[pos2*n + j] = tmp_b;
}
}
/* ...and variables (column)*/
if(pos1 != pos3)
{
tmp_i = Index_Var_IM[pos1].index;
Index_Var_IM[pos1].index = Index_Var_IM[pos3].index;
Index_Var_IM[pos3].index = tmp_i;
for(j = 0;j < n;j++)
{
tmp_b = SIM[j * n + pos1];
SIM[j*n + pos1] = SIM[j * n + pos3];
SIM[j*n + pos3] = tmp_b;
}
}
}
//------------------------------------------------------------------------------
// Find the prologue and the epilogue of the model
void
BlockTriangular::Prologue_Epilogue(bool* IM, int* prologue, int* epilogue, int n, simple* Index_Var_IM, simple* Index_Equ_IM, bool* IM0)
{
bool modifie = 1;
int i, j, k, l = 0;
/*Looking for a prologue */
*prologue = 0;
while(modifie)
{
modifie = 0;
for(i = *prologue;i < n;i++)
{
k = 0;
for(j = *prologue;j < n;j++)
{
if(IM[i*n + j])
{
k++;
l = j;
}
}
if ((k == 1) && IM0[Index_Equ_IM[i].index*n + Index_Var_IM[l].index])
{
modifie = 1;
swap_IM_c(IM, *prologue, i, l, Index_Var_IM, Index_Equ_IM, n);
Index_Equ_IM[*prologue].available = 0;
Index_Var_IM[*prologue].available = 0;
(*prologue)++;
}
}
}
*epilogue = 0;
modifie = 1; /*print_SIM(IM,n);
print_SIM(IM*/
while(modifie)
{
modifie = 0;
for(i = *prologue;i < n - *epilogue;i++)
{
k = 0;
for(j = *prologue;j < n - *epilogue;j++)
{
if(IM[j*n + i])
{
k++;
l = j;
}
}
if ((k == 1) && IM0[Index_Equ_IM[l].index*n + Index_Var_IM[i].index])
{
modifie = 1;
swap_IM_c(IM, n - (1 + *epilogue), l, i, Index_Var_IM, Index_Equ_IM, n);
Index_Equ_IM[n - (1 + *epilogue)].available = 0;
Index_Var_IM[n - (1 + *epilogue)].available = 0;
(*epilogue)++;
}
}
}
}
void
BlockTriangular::Allocate_Block(int size, int *count_Equ, int *count_Block, int type, Model_Block * ModelBlock)
{
int i, j, k, l, ls, m, i_1, Lead, Lag, size_list_lead_var, first_count_equ, i1;
int *list_lead_var, *tmp_size, *tmp_var, *tmp_endo, nb_lead_lag_endo;
List_IM *Cur_IM;
bool *IM, OK;
ModelBlock->Periods = periods;
if ((type == PROLOGUE) || (type == EPILOGUE))
{
for(i = 0;i < size;i++)
{
ModelBlock->Block_List[*count_Block].is_linear=true;
ModelBlock->Block_List[*count_Block].Size = 1;
ModelBlock->Block_List[*count_Block].Type = type;
ModelBlock->Block_List[*count_Block].Simulation_Type = UNKNOWN;
ModelBlock->Block_List[*count_Block].Temporary_terms=new temporary_terms_type ();
ModelBlock->Block_List[*count_Block].Temporary_terms->clear();
list_lead_var = (int*)malloc(Model_Max_Lead * endo_nbr * sizeof(int));
size_list_lead_var = 0;
tmp_endo = (int*)malloc((Model_Max_Lead + Model_Max_Lag + 1) * sizeof(int));
tmp_size = (int*)malloc((Model_Max_Lead + Model_Max_Lag + 1) * sizeof(int));
tmp_var = (int*)malloc(sizeof(int));
memset(tmp_size, 0, (Model_Max_Lead + Model_Max_Lag + 1)*sizeof(int));
memset(tmp_endo, 0, (Model_Max_Lead + Model_Max_Lag + 1)*sizeof(int));
nb_lead_lag_endo = Lead = Lag = 0;
Cur_IM = First_IM;
while(Cur_IM)
{
k = Cur_IM->lead_lag;
i_1 = Index_Equ_IM[*count_Equ].index * endo_nbr;
if(k > 0)
{
if(Cur_IM->IM[i_1 + Index_Var_IM[ /*j*/*count_Equ].index])
{
nb_lead_lag_endo++;
tmp_size[Model_Max_Lag + k]++;
if(k > Lead)
{
Lead = k;
list_lead_var[size_list_lead_var] = Index_Var_IM[*count_Equ].index + size * (k - 1); size_list_lead_var++;
}
}
}
else
{
k = -k;
if(Cur_IM->IM[i_1 + Index_Var_IM[ /*j*/*count_Equ].index])
{
tmp_size[Model_Max_Lag - k]++;
nb_lead_lag_endo++;
if(k > Lag)
{
Lag = k;
}
}
}
Cur_IM = Cur_IM->pNext;
}
ModelBlock->Block_List[*count_Block].Max_Lag = Lag;
ModelBlock->Block_List[*count_Block].Max_Lead = Lead;
free(list_lead_var);
ModelBlock->Block_List[*count_Block].Equation = (int*)malloc(sizeof(int));
ModelBlock->Block_List[*count_Block].Variable = (int*)malloc(sizeof(int));
ModelBlock->Block_List[*count_Block].Variable_Sorted = (int*)malloc(sizeof(int));
ModelBlock->Block_List[*count_Block].Own_Derivative = (int*)malloc(sizeof(int));
ModelBlock->Block_List[*count_Block].Equation[0] = Index_Equ_IM[*count_Equ].index;
ModelBlock->Block_List[*count_Block].Variable[0] = Index_Var_IM[*count_Equ].index;
ModelBlock->Block_List[*count_Block].Variable_Sorted[0] = -1;
ModelBlock->in_Block_Equ[Index_Equ_IM[*count_Equ].index] = *count_Block;
ModelBlock->in_Block_Var[Index_Var_IM[*count_Equ].index] = *count_Block;
ModelBlock->in_Equ_of_Block[Index_Equ_IM[*count_Equ].index] = ModelBlock->in_Var_of_Block[Index_Var_IM[*count_Equ].index] = 0;
Index_Equ_IM[*count_Equ].block = *count_Block;
if ((Lead > 0) && (Lag > 0))
ModelBlock->Block_List[*count_Block].Simulation_Type = SOLVE_TWO_BOUNDARIES_SIMPLE;
else if((Lead > 0) && (Lag == 0))
ModelBlock->Block_List[*count_Block].Simulation_Type = SOLVE_BACKWARD_SIMPLE;
else
ModelBlock->Block_List[*count_Block].Simulation_Type = SOLVE_FOREWARD_SIMPLE;
ModelBlock->Block_List[*count_Block].IM_lead_lag = (IM_compact*)malloc((Lead + Lag + 1) * sizeof(IM_compact));
ModelBlock->Block_List[*count_Block].Nb_Lead_Lag_Endo = nb_lead_lag_endo;
if(nb_lead_lag_endo)
{
ModelBlock->Block_List[*count_Block].variable_dyn_index = (int*)malloc(nb_lead_lag_endo * sizeof(int));
ModelBlock->Block_List[*count_Block].variable_dyn_leadlag = (int*)malloc(nb_lead_lag_endo * sizeof(int));
}
ls = l = 1;
i1 = 0;
for(int li = 0;li < Lead + Lag + 1;li++)
{
ModelBlock->Block_List[*count_Block].IM_lead_lag[li].size = tmp_size[Model_Max_Lag - Lag + li];
if(tmp_size[Model_Max_Lag - Lag + li])
{
ModelBlock->Block_List[*count_Block].IM_lead_lag[li].nb_endo = tmp_size[Model_Max_Lag - Lag + li];
ModelBlock->Block_List[*count_Block].IM_lead_lag[li].u = (int*)malloc(tmp_size[Model_Max_Lag - Lag + li] * sizeof(int));
ModelBlock->Block_List[*count_Block].IM_lead_lag[li].us = (int*)malloc(tmp_size[Model_Max_Lag - Lag + li] * sizeof(int));
ModelBlock->Block_List[*count_Block].IM_lead_lag[li].Var = (int*)malloc(tmp_size[Model_Max_Lag - Lag + li] * sizeof(int));
ModelBlock->Block_List[*count_Block].IM_lead_lag[li].Equ = (int*)malloc(tmp_size[Model_Max_Lag - Lag + li] * sizeof(int));
ModelBlock->Block_List[*count_Block].IM_lead_lag[li].Var_Index = (int*)malloc(tmp_size[Model_Max_Lag - Lag + li] * sizeof(int));
ModelBlock->Block_List[*count_Block].IM_lead_lag[li].Equ_Index = (int*)malloc(tmp_size[Model_Max_Lag - Lag + li] * sizeof(int));
ModelBlock->Block_List[*count_Block].IM_lead_lag[li].Var_dyn_Index = (int*)malloc(tmp_size[Model_Max_Lag - Lag + li] * sizeof(int));
ModelBlock->Block_List[*count_Block].IM_lead_lag[li].u_init = l;
IM = bGet_IM(li - Lag);
if(IM == NULL)
{
cout << "Error IM(" << li - Lag << ") doesn't exist\n";
exit( -1);
}
if(IM[Index_Var_IM[*count_Equ].index + Index_Equ_IM[*count_Equ].index*endo_nbr] && nb_lead_lag_endo)
{ ModelBlock->Block_List[*count_Block].variable_dyn_index[i1] = Index_Var_IM[*count_Equ].index;
ModelBlock->Block_List[*count_Block].variable_dyn_leadlag[i1] = li - Lag;
tmp_var[0] = i1;
i1++;
}
m = 0;
i_1 = Index_Equ_IM[*count_Equ].index * endo_nbr;
if(IM[Index_Var_IM[*count_Equ].index + i_1])
{
if(li == Lag)
{
ModelBlock->Block_List[*count_Block].IM_lead_lag[li].us[m] = ls;
ls++;
}
ModelBlock->Block_List[*count_Block].IM_lead_lag[li].u[m] = l;
ModelBlock->Block_List[*count_Block].IM_lead_lag[li].Equ[m] = 0;
ModelBlock->Block_List[*count_Block].IM_lead_lag[li].Var[m] = 0;
ModelBlock->Block_List[*count_Block].IM_lead_lag[li].Equ_Index[m] = Index_Equ_IM[*count_Equ].index;
ModelBlock->Block_List[*count_Block].IM_lead_lag[li].Var_Index[m] = Index_Var_IM[*count_Equ].index;
ModelBlock->Block_List[*count_Block].IM_lead_lag[li].Var_dyn_Index[m] = ModelBlock->Block_List[*count_Block].variable_dyn_index[tmp_var[0]];
l++;
m++;
}
ModelBlock->Block_List[*count_Block].IM_lead_lag[li].u_finish = l - 1;
}
}
(*count_Equ)++;
(*count_Block)++;
free(tmp_size);
free(tmp_endo);
free(tmp_var);
}
}
else
{
ModelBlock->Block_List[*count_Block].is_linear=true;
ModelBlock->Block_List[*count_Block].Size = size;
ModelBlock->Block_List[*count_Block].Type = type;
ModelBlock->Block_List[*count_Block].Temporary_terms=new temporary_terms_type ();
ModelBlock->Block_List[*count_Block].Temporary_terms->clear();
ModelBlock->Block_List[*count_Block].Simulation_Type = UNKNOWN;
ModelBlock->Block_List[*count_Block].Equation = (int*)malloc(ModelBlock->Block_List[*count_Block].Size * sizeof(int));
ModelBlock->Block_List[*count_Block].Variable = (int*)malloc(ModelBlock->Block_List[*count_Block].Size * sizeof(int));
ModelBlock->Block_List[*count_Block].Variable_Sorted = (int*)malloc(ModelBlock->Block_List[*count_Block].Size * sizeof(int));
ModelBlock->Block_List[*count_Block].Own_Derivative = (int*)malloc(ModelBlock->Block_List[*count_Block].Size * sizeof(int));
Lead = Lag = 0;
first_count_equ = *count_Equ;
tmp_var = (int*)malloc(size * sizeof(int));
tmp_endo = (int*)malloc((Model_Max_Lead + Model_Max_Lag + 1) * sizeof(int));
tmp_size = (int*)malloc((Model_Max_Lead + Model_Max_Lag + 1) * sizeof(int));
memset(tmp_size, 0, (Model_Max_Lead + Model_Max_Lag + 1)*sizeof(int));
memset(tmp_endo, 0, (Model_Max_Lead + Model_Max_Lag + 1)*sizeof(int));
nb_lead_lag_endo = 0;
for(i = 0;i < size;i++)
{
Index_Equ_IM[*count_Equ].block = *count_Block;
ModelBlock->Block_List[*count_Block].Equation[i] = Index_Equ_IM[*count_Equ].index;
ModelBlock->Block_List[*count_Block].Variable[i] = Index_Var_IM[*count_Equ].index;
ModelBlock->Block_List[*count_Block].Variable_Sorted[i] = -1;
ModelBlock->in_Block_Equ[Index_Equ_IM[*count_Equ].index] = *count_Block;
ModelBlock->in_Block_Var[Index_Var_IM[*count_Equ].index] = *count_Block;
ModelBlock->in_Equ_of_Block[Index_Equ_IM[*count_Equ].index] = ModelBlock->in_Var_of_Block[Index_Var_IM[*count_Equ].index] = i;
Cur_IM = First_IM;
i_1 = Index_Var_IM[*count_Equ].index;
while(Cur_IM)
{
k = Cur_IM->lead_lag;
OK = false;
if(k >= 0)
{ for(j = 0;j < size;j++)
{
if(Cur_IM->IM[i_1 + Index_Equ_IM[first_count_equ + j].index*endo_nbr])
{
tmp_size[Model_Max_Lag + k]++;
if (!OK)
{
tmp_endo[Model_Max_Lag + k]++;
nb_lead_lag_endo++;
OK = true;
}
if(k > Lead)
Lead = k;
}
}
}
else
{
k = -k;
for(j = 0;j < size;j++)
{
if(Cur_IM->IM[i_1 + Index_Equ_IM[first_count_equ + j].index*endo_nbr])
{
tmp_size[Model_Max_Lag - k]++;
if (!OK)
{
tmp_endo[Model_Max_Lag - k]++;
nb_lead_lag_endo++;
OK = true;
}
if(k > Lag)
Lag = k;
}
}
}
Cur_IM = Cur_IM->pNext;
}
(*count_Equ)++;
}
if ((Lag > 0) && (Lead > 0))
ModelBlock->Block_List[*count_Block].Simulation_Type = SOLVE_TWO_BOUNDARIES_COMPLETE;
else if(size > 1)
{
if(Lead > 0)
ModelBlock->Block_List[*count_Block].Simulation_Type = SOLVE_BACKWARD_COMPLETE;
else
ModelBlock->Block_List[*count_Block].Simulation_Type = SOLVE_FOREWARD_COMPLETE;
}
else
{
if(Lead > 0)
ModelBlock->Block_List[*count_Block].Simulation_Type = SOLVE_BACKWARD_SIMPLE;
else
ModelBlock->Block_List[*count_Block].Simulation_Type = SOLVE_FOREWARD_SIMPLE;
}
ModelBlock->Block_List[*count_Block].Max_Lag = Lag;
ModelBlock->Block_List[*count_Block].Max_Lead = Lead;
ModelBlock->Block_List[*count_Block].IM_lead_lag = (IM_compact*)malloc((Lead + Lag + 1) * sizeof(IM_compact));
ls = l = size;
i1 = 0;
ModelBlock->Block_List[*count_Block].Nb_Lead_Lag_Endo = nb_lead_lag_endo;
ModelBlock->Block_List[*count_Block].variable_dyn_index = (int*)malloc(nb_lead_lag_endo * sizeof(int));
ModelBlock->Block_List[*count_Block].variable_dyn_leadlag = (int*)malloc(nb_lead_lag_endo * sizeof(int));
for(i = 0;i < Lead + Lag + 1;i++)
{
ModelBlock->Block_List[*count_Block].IM_lead_lag[i].size = tmp_size[Model_Max_Lag - Lag + i];
ModelBlock->Block_List[*count_Block].IM_lead_lag[i].nb_endo = tmp_endo[Model_Max_Lag - Lag + i];
ModelBlock->Block_List[*count_Block].IM_lead_lag[i].u = (int*)malloc(tmp_size[Model_Max_Lag - Lag + i] * sizeof(int));
ModelBlock->Block_List[*count_Block].IM_lead_lag[i].us = (int*)malloc(tmp_size[Model_Max_Lag - Lag + i] * sizeof(int));
ModelBlock->Block_List[*count_Block].IM_lead_lag[i].Var = (int*)malloc(tmp_size[Model_Max_Lag - Lag + i] * sizeof(int)); ModelBlock->Block_List[*count_Block].IM_lead_lag[i].Equ = (int*)malloc(tmp_size[Model_Max_Lag - Lag + i] * sizeof(int));
ModelBlock->Block_List[*count_Block].IM_lead_lag[i].Var_Index = (int*)malloc(tmp_size[Model_Max_Lag - Lag + i] * sizeof(int));
ModelBlock->Block_List[*count_Block].IM_lead_lag[i].Equ_Index = (int*)malloc(tmp_size[Model_Max_Lag - Lag + i] * sizeof(int));
ModelBlock->Block_List[*count_Block].IM_lead_lag[i].Var_dyn_Index = (int*)malloc(tmp_size[Model_Max_Lag - Lag + i] * sizeof(int));
ModelBlock->Block_List[*count_Block].IM_lead_lag[i].u_init = l;
IM = bGet_IM(i - Lag);
if(IM != NULL)
{
}
else
{
cout << "Error IM(" << i - Lag << ") doesn't exist\n";
exit( -1);
}
for(j = first_count_equ;j < size + first_count_equ;j++)
{
i_1 = Index_Var_IM[j].index;
m = 0;
for(k = first_count_equ;k < size + first_count_equ;k++)
if(IM[i_1 + Index_Equ_IM[k].index*endo_nbr])
m++;
if(m > 0)
{
ModelBlock->Block_List[*count_Block].variable_dyn_index[i1] = i_1;
ModelBlock->Block_List[*count_Block].variable_dyn_leadlag[i1] = i - Lag;
tmp_var[j - first_count_equ] = i1;
i1++;
}
}
m = 0;
for(j = first_count_equ;j < size + first_count_equ;j++)
{
i_1 = Index_Equ_IM[j].index * endo_nbr;
for(k = first_count_equ;k < size + first_count_equ;k++)
if(IM[Index_Var_IM[k].index + i_1])
{
if(i == Lag)
{
ModelBlock->Block_List[*count_Block].IM_lead_lag[i].us[m] = ls;
ls++;
#ifdef DEBUG
printf("j=%d ModelBlock->Block_List[%d].Variable[%d]=%d Index_Var_IM[%d].index=%d", j, *count_Block, j - first_count_equ, ModelBlock->Block_List[*count_Block].Variable[j - first_count_equ], k, Index_Var_IM[k].index);
if(ModelBlock->Block_List[*count_Block].Variable[j - first_count_equ] == Index_Var_IM[k].index)
{
ModelBlock->Block_List[*count_Block].Own_Derivative[j - first_count_equ]=l;
printf(" l=%d OK\n",l);
}
else
printf("\n");
#endif
}
ModelBlock->Block_List[*count_Block].IM_lead_lag[i].u[m] = l;
ModelBlock->Block_List[*count_Block].IM_lead_lag[i].Equ[m] = j - first_count_equ;
ModelBlock->Block_List[*count_Block].IM_lead_lag[i].Var[m] = k - first_count_equ;
ModelBlock->Block_List[*count_Block].IM_lead_lag[i].Equ_Index[m] = Index_Equ_IM[j].index;
ModelBlock->Block_List[*count_Block].IM_lead_lag[i].Var_Index[m] = Index_Var_IM[k].index;
ModelBlock->Block_List[*count_Block].IM_lead_lag[i].Var_dyn_Index[m] = ModelBlock->Block_List[*count_Block].variable_dyn_index[tmp_var[k - first_count_equ]];
l++;
m++;
}
}
ModelBlock->Block_List[*count_Block].IM_lead_lag[i].u_finish = l - 1;
}
(*count_Block)++;
free(tmp_size);
free(tmp_endo);
free(tmp_var);
}
}
void
BlockTriangular::Free_Block(Model_Block* ModelBlock) const
{
int blk, i;
#ifdef DEBUG
cout << "Free_Block\n";
#endif
for(blk = 0;blk < ModelBlock->Size;blk++)
{
if ((ModelBlock->Block_List[blk].Type == PROLOGUE) || (ModelBlock->Block_List[blk].Type == EPILOGUE))
{
free(ModelBlock->Block_List[blk].Equation);
free(ModelBlock->Block_List[blk].Variable);
free(ModelBlock->Block_List[blk].Variable_Sorted);
free(ModelBlock->Block_List[blk].Own_Derivative);
if(ModelBlock->Block_List[blk].Nb_Lead_Lag_Endo)
{
free(ModelBlock->Block_List[blk].variable_dyn_index);
free(ModelBlock->Block_List[blk].variable_dyn_leadlag);
}
for(i = 0;i < ModelBlock->Block_List[blk].Max_Lag + ModelBlock->Block_List[blk].Max_Lead + 1;i++)
{
free(ModelBlock->Block_List[blk].IM_lead_lag[i].u);
free(ModelBlock->Block_List[blk].IM_lead_lag[i].us);
free(ModelBlock->Block_List[blk].IM_lead_lag[i].Var);
free(ModelBlock->Block_List[blk].IM_lead_lag[i].Equ);
free(ModelBlock->Block_List[blk].IM_lead_lag[i].Var_Index);
free(ModelBlock->Block_List[blk].IM_lead_lag[i].Equ_Index);
free(ModelBlock->Block_List[blk].IM_lead_lag[i].Var_dyn_Index);
}
free(ModelBlock->Block_List[blk].IM_lead_lag);
delete(ModelBlock->Block_List[blk].Temporary_terms);
}
else
{
free(ModelBlock->Block_List[blk].Equation);
free(ModelBlock->Block_List[blk].Variable);
free(ModelBlock->Block_List[blk].Variable_Sorted);
free(ModelBlock->Block_List[blk].Own_Derivative);
free(ModelBlock->Block_List[blk].variable_dyn_index);
free(ModelBlock->Block_List[blk].variable_dyn_leadlag);
for(i = 0;i < ModelBlock->Block_List[blk].Max_Lag + ModelBlock->Block_List[blk].Max_Lead + 1;i++)
{
free(ModelBlock->Block_List[blk].IM_lead_lag[i].u);
free(ModelBlock->Block_List[blk].IM_lead_lag[i].us);
free(ModelBlock->Block_List[blk].IM_lead_lag[i].Equ);
free(ModelBlock->Block_List[blk].IM_lead_lag[i].Var);
free(ModelBlock->Block_List[blk].IM_lead_lag[i].Equ_Index);
free(ModelBlock->Block_List[blk].IM_lead_lag[i].Var_Index);
free(ModelBlock->Block_List[blk].IM_lead_lag[i].Var_dyn_Index);
}
free(ModelBlock->Block_List[blk].IM_lead_lag);
delete(ModelBlock->Block_List[blk].Temporary_terms);
}
}
free(ModelBlock->in_Block_Equ);
free(ModelBlock->in_Block_Var);
free(ModelBlock->in_Equ_of_Block);
free(ModelBlock->in_Var_of_Block);
free(ModelBlock->Block_List);
free(ModelBlock);
free(Index_Equ_IM);
free(Index_Var_IM);
}
//------------------------------------------------------------------------------// Normalize each equation of the model (endgenous_i = f_i(endogenous_1, ..., endogenous_n) - in order to apply strong connex components search algorithm -
// and find the optimal blocks triangular decomposition
bool
BlockTriangular::Normalize_and_BlockDecompose(bool* IM, Model_Block* ModelBlock, int n, int* prologue, int* epilogue, simple* Index_Var_IM, simple* Index_Equ_IM, bool Do_Normalization, bool mixing, bool* IM_0, jacob_map j_m )
{
int i, j, Nb_TotalBlocks, Nb_RecursBlocks;
int count_Block, count_Equ;
block_result_t* res;
//List_IM * p_First_IM, *p_Cur_IM, *Cur_IM;
Equation_set* Equation_gr = (Equation_set*) malloc(sizeof(Equation_set));
bool* SIM0, *SIM00;
/*p_First_IM = (List_IM*)malloc(sizeof(*p_First_IM));
p_Cur_IM = p_First_IM;
Cur_IM = First_IM;
i = endo_nbr * endo_nbr;
while(Cur_IM)
{
p_Cur_IM->lead_lag = Cur_IM->lead_lag;
p_Cur_IM->IM = (bool*)malloc(i * sizeof(bool));
memcpy ( p_Cur_IM->IM, Cur_IM->IM, i);
Cur_IM = Cur_IM->pNext;
if(Cur_IM)
{
p_Cur_IM->pNext = (List_IM*)malloc(sizeof(*p_Cur_IM));
p_Cur_IM = p_Cur_IM->pNext;
}
else
p_Cur_IM->pNext = NULL;
}*/
SIM0 = (bool*)malloc(n * n * sizeof(bool));
//cout << "Allocate SIM0=" << SIM0 << " size=" << n * n * sizeof(bool) << "\n";
memcpy(SIM0,IM_0,n*n*sizeof(bool));
Prologue_Epilogue(IM, prologue, epilogue, n, Index_Var_IM, Index_Equ_IM, SIM0);
//cout << "free SIM0=" << SIM0 << "\n";
free(SIM0);
if(bt_verbose)
{
cout << "prologue : " << *prologue << " epilogue : " << *epilogue << "\n";
cout << "IM_0\n";
Print_SIM(IM_0, n);
cout << "IM\n";
Print_SIM(IM, n);
for(i = 0;i < n;i++)
cout << "Index_Var_IM[" << i << "]=" << Index_Var_IM[i].index << " Index_Equ_IM[" << i << "]=" << Index_Equ_IM[i].index << "\n";
}
if(*prologue+*epilogue<n)
{
if(Do_Normalization)
{
cout << "Normalizing the model ...\n";
if(mixing)
{
double* max_val=(double*)malloc(n*sizeof(double));
//cout << "n=" << n << "\n";
memset(max_val,0,n*sizeof(double));
for( map< pair< int, int >, double >::iterator iter = j_m.begin(); iter != j_m.end(); iter++ )
{
//cout << "iter->first.first=" << iter->first.first << "\n";
if(fabs(iter->second)>max_val[iter->first.first])
max_val[iter->first.first]=fabs(iter->second);
}
for( map< pair< int, int >, double >::iterator iter = j_m.begin(); iter != j_m.end(); iter++ )
iter->second/=max_val[iter->first.first];
free(max_val);
bool OK=false;
double bi=0.99999999;
int suppressed=0;
while(!OK && bi>1e-14)
{
int suppress=0; SIM0 = (bool*)malloc(n * n * sizeof(bool));
//cout << "Allocate SIM0=" << SIM0 << " size=" << n * n * sizeof(bool) << "\n";
memset(SIM0,0,n*n*sizeof(bool));
SIM00 = (bool*)malloc(n * n * sizeof(bool));
//cout << "Allocate SIM00=" << SIM00 << " size=" << n * n * sizeof(bool) << "\n";
memset(SIM00,0,n*n*sizeof(bool));
//cout << "n*n=" << n*n << "\n";
for( map< pair< int, int >, double >::iterator iter = j_m.begin(); iter != j_m.end(); iter++ )
{
if(fabs(iter->second)>bi)
{
//cout << "iter->first.first*n+iter->first.second=" << iter->first.first*n+iter->first.second << "\n";
SIM0[iter->first.first*n+iter->first.second]=1;
if(!IM_0[iter->first.first*n+iter->first.second])
{
cout << "Error nothing at IM_0[" << iter->first.first << ", " << iter->first.second << "]=" << IM_0[iter->first.first*n+iter->first.second] << "\n";
}
}
else
suppress++;
}
//cout << "n*n=" << n*n << "\n";
for(i = 0;i < n;i++)
for(j = 0;j < n;j++)
{
//cout << "Index_Equ_IM[i].index * n + Index_Var_IM[j].index=" << Index_Equ_IM[i].index * n + Index_Var_IM[j].index << "\n";
SIM00[i*n + j] = SIM0[Index_Equ_IM[i].index * n + Index_Var_IM[j].index];
}
//cout << "free SIM0=" << SIM0 << "\n";
free(SIM0);
if(suppress!=suppressed)
{
OK=normalization.Normalize(n, *prologue, *epilogue, SIM00, Index_Equ_IM, Equation_gr, 1, IM);
if(!OK)
normalization.Free_Equation(n-*prologue-*epilogue,Equation_gr);
}
suppressed=suppress;
if(!OK)
bi/=1.07;
if(bi>1e-14)
free(SIM00);
}
if(!OK)
{
normalization.Set_fp_verbose(true);
OK=normalization.Normalize(n, *prologue, *epilogue, SIM00, Index_Equ_IM, Equation_gr, 1, IM);
cout << "Error\n";
exit(-1);
}
}
else
normalization.Normalize(n, *prologue, *epilogue, IM, Index_Equ_IM, Equation_gr, 0, 0);
}
else
normalization.Gr_to_IM_basic(n, *prologue, *epilogue, IM, Equation_gr, false);
}
cout << "Finding the optimal block decomposition of the model ...\n";
if(*prologue+*epilogue<n)
{
if(bt_verbose)
blocks.Print_Equation_gr(Equation_gr);
res = blocks.sc(Equation_gr);
normalization.Free_Equation(n-*prologue-*epilogue,Equation_gr);
if(bt_verbose)
blocks.block_result_print(res);
}
else
{
res = (block_result_t*)malloc(sizeof(*res));
res->n_sets=0; }
free(Equation_gr);
if ((*prologue) || (*epilogue))
j = 1;
else
j = 0;
for(i = 0;i < res->n_sets;i++)
{
if ((res->sets_f[i] - res->sets_s[i] + 1) > j)
j = res->sets_f[i] - res->sets_s[i] + 1;
}
Nb_RecursBlocks = *prologue + *epilogue;
Nb_TotalBlocks = res->n_sets + Nb_RecursBlocks;
cout << Nb_TotalBlocks << " block(s) found:\n";
cout << " " << Nb_RecursBlocks << " recursive block(s) and " << res->n_sets << " simultaneous block(s). \n";
cout << " the largest simultaneous block has " << j << " equation(s). \n";
ModelBlock->Size = Nb_TotalBlocks;
ModelBlock->Periods = periods;
ModelBlock->in_Block_Equ = (int*)malloc(n * sizeof(int));
ModelBlock->in_Block_Var = (int*)malloc(n * sizeof(int));
ModelBlock->in_Equ_of_Block = (int*)malloc(n * sizeof(int));
ModelBlock->in_Var_of_Block = (int*)malloc(n * sizeof(int));
ModelBlock->Block_List = (Block*)malloc(sizeof(ModelBlock->Block_List[0]) * Nb_TotalBlocks);
blocks.block_result_to_IM(res, IM, *prologue, endo_nbr, Index_Equ_IM, Index_Var_IM);
//Free_IM(p_First_IM);
count_Equ = count_Block = 0;
//Print_IM(endo_nbr);
if (*prologue)
Allocate_Block(*prologue, &count_Equ, &count_Block, PROLOGUE, ModelBlock);
for(j = 0;j < res->n_sets;j++)
{
if(res->sets_f[res->ordered[j]] == res->sets_s[res->ordered[j]])
Allocate_Block(res->sets_f[res->ordered[j]] - res->sets_s[res->ordered[j]] + 1, &count_Equ, &count_Block, PROLOGUE, ModelBlock);
else
Allocate_Block(res->sets_f[res->ordered[j]] - res->sets_s[res->ordered[j]] + 1, &count_Equ, &count_Block, SIMULTANS, ModelBlock);
}
if (*epilogue)
Allocate_Block(*epilogue, &count_Equ, &count_Block, EPILOGUE, ModelBlock);
if(res->n_sets)
blocks.block_result_free(res);
else
free(res);
return 0;
}
//------------------------------------------------------------------------------
// normalize each equation of the dynamic model
// and find the optimal block triangular decomposition of the static model
void
BlockTriangular::Normalize_and_BlockDecompose_Static_0_Model(const jacob_map &j_m)
{
bool* SIM, *SIM_0;
List_IM* Cur_IM;
int i;
//First create a static model incidence matrix
SIM = (bool*)malloc(endo_nbr * endo_nbr * sizeof(*SIM));
for(i = 0;i < endo_nbr*endo_nbr;i++)
{
SIM[i] = 0;
Cur_IM = First_IM;
while(Cur_IM)
{
SIM[i] = (SIM[i]) || (Cur_IM->IM[i]);
Cur_IM = Cur_IM->pNext;
}
}
if(bt_verbose)
{
cout << "incidence matrix for the static model (unsorted) \n";
Print_SIM(SIM, endo_nbr); }
Index_Equ_IM = (simple*)malloc(endo_nbr * sizeof(*Index_Equ_IM));
for(i = 0;i < endo_nbr;i++)
{
Index_Equ_IM[i].index = i;
Index_Equ_IM[i].available = 1;
}
Index_Var_IM = (simple*)malloc(endo_nbr * sizeof(*Index_Var_IM));
for(i = 0;i < endo_nbr;i++)
{
Index_Var_IM[i].index = i;
Index_Var_IM[i].available = 1;
}
if(ModelBlock != NULL)
Free_Block(ModelBlock);
ModelBlock = (Model_Block*)malloc(sizeof(*ModelBlock));
Cur_IM = Get_IM(0);
SIM_0 = (bool*)malloc(endo_nbr * endo_nbr * sizeof(*SIM_0));
for(i = 0;i < endo_nbr*endo_nbr;i++)
SIM_0[i] = Cur_IM->IM[i];
Normalize_and_BlockDecompose(SIM, ModelBlock, endo_nbr, &prologue, &epilogue, Index_Var_IM, Index_Equ_IM, 1, 1, SIM_0, j_m);
free(SIM_0);
free(SIM);
if(bt_verbose)
for(i = 0;i < endo_nbr;i++)
cout << "Block=" << Index_Equ_IM[i].block << " Equ=" << Index_Equ_IM[i].index << " Var= " << Index_Var_IM[i].index << " " << symbol_table.getNameByID(eEndogenous, Index_Var_IM[i].index) << "\n";
}