Commit 05dca0e3 authored by Sébastien Villemot's avatar Sébastien Villemot
Browse files

Change the default algorithm for stack_solve_algo = 0

The old algorithm (LBJ) is now available under stack_stock_algo = 6
parent 4b86df05
......@@ -2795,7 +2795,7 @@ Algorithm used for computing the solution. Possible values are:
@item 0
Newton method to solve simultaneously all the equations for every
period, see @cite{Juillard (1996)} (Default).
period, using sparse matrices (Default).
@item 1
Use a Newton algorithm with a sparse LU solver at each iteration
......@@ -2821,6 +2821,13 @@ declaration}).
Use a Newton algorithm with a sparse Gaussian elimination (SPE) solver
at each iteration (requires @code{bytecode} option, @pxref{Model
declaration}).
@item 6
Use the historical algorithm proposed in @cite{Juillard (1996)}: it is
slower than @code{stack_solve_algo=0}, but may be less memory consuming
on big models (not available with @code{bytecode} and/or @code{block}
options).
@end table
@item markowitz = @var{DOUBLE}
......
function sim1
% function sim1
% performs deterministic simulations with lead or lag on one period
% Performs deterministic simulations with lead or lag on one period.
% Uses sparse matrices.
%
% INPUTS
% ...
% OUTPUTS
% ...
% ALGORITHM
% Laffargue, Boucekkine, Juillard (LBJ)
% see Juillard (1996) Dynare: A program for the resolution and
% simulation of dynamic models with forward variables through the use
% of a relaxation algorithm. CEPREMAP. Couverture Orange. 9602.
% ...
%
% SPECIAL REQUIREMENTS
% None.
% Copyright (C) 1996-2010 Dynare Team
% Copyright (C) 1996-2012 Dynare Team
%
% This file is part of Dynare.
%
......@@ -36,13 +34,19 @@ global M_ options_ oo_
lead_lag_incidence = M_.lead_lag_incidence;
ny = size(oo_.endo_simul,1) ;
ny = M_.endo_nbr;
max_lag = M_.maximum_endo_lag;
nyp = nnz(lead_lag_incidence(1,:)) ;
iyp = find(lead_lag_incidence(1,:)>0) ;
ny0 = nnz(lead_lag_incidence(2,:)) ;
iy0 = find(lead_lag_incidence(2,:)>0) ;
nyf = nnz(lead_lag_incidence(3,:)) ;
nrs = ny+nyp+nyf+1 ;
nrc = nyf+1 ;
iyf = find(lead_lag_incidence(3,:)>0) ;
iyp = find(lead_lag_incidence(1,:)>0) ;
nd = nyp+ny0+nyf;
nrc = nyf+1 ;
isp = [1:nyp] ;
is = [nyp+1:ny+nyp] ;
isf = iyf+nyp ;
......@@ -50,57 +54,63 @@ isf1 = [nyp+ny+1:nyf+nyp+ny+1] ;
stop = 0 ;
iz = [1:ny+nyp+nyf];
periods = options_.periods
steady_state = oo_.steady_state;
params = M_.params;
endo_simul = oo_.endo_simul;
exo_simul = oo_.exo_simul;
i_cols_1 = nonzeros(lead_lag_incidence(2:3,:)');
i_cols_A1 = find(lead_lag_incidence(2:3,:)');
i_cols_T = nonzeros(lead_lag_incidence(1:2,:)');
i_cols_j = 1:nd;
i_upd = ny+(1:periods*ny);
Y = endo_simul(:);
disp (['-----------------------------------------------------']) ;
disp (['MODEL SIMULATION :']) ;
fprintf('\n') ;
it_init = M_.maximum_lag+1 ;
model_dynamic = str2func([M_.fname,'_dynamic']);
z = Y(find(lead_lag_incidence'));
[d1,jacobian] = model_dynamic(z,oo_.exo_simul, params, ...
steady_state,2);
A = sparse([],[],[],periods*ny,periods*ny,periods*nnz(jacobian));
res = zeros(periods*ny,1);
h1 = clock ;
for iter = 1:options_.maxit_
h2 = clock ;
if options_.terminal_condition == 0
c = zeros(ny*options_.periods,nrc) ;
else
c = zeros(ny*(options_.periods+1),nrc) ;
end
it_ = it_init ;
z = [oo_.endo_simul(iyp,it_-1) ; oo_.endo_simul(:,it_) ; oo_.endo_simul(iyf,it_+1)] ;
[d1,jacobian] = feval([M_.fname '_dynamic'],z,oo_.exo_simul, M_.params, oo_.steady_state,it_);
jacobian = [jacobian(:,iz) -d1] ;
ic = [1:ny] ;
icp = iyp ;
c (ic,:) = jacobian(:,is)\jacobian(:,isf1) ;
for it_ = it_init+(1:options_.periods-1)
z = [oo_.endo_simul(iyp,it_-1) ; oo_.endo_simul(:,it_) ; oo_.endo_simul(iyf,it_+1)] ;
[d1,jacobian] = feval([M_.fname '_dynamic'],z,oo_.exo_simul, ...
M_.params, oo_.steady_state, it_);
jacobian = [jacobian(:,iz) -d1] ;
jacobian(:,[isf nrs]) = jacobian(:,[isf nrs])-jacobian(:,isp)*c(icp,:) ;
ic = ic + ny ;
icp = icp + ny ;
c (ic,:) = jacobian(:,is)\jacobian(:,isf1) ;
end
i_rows = 1:ny;
i_cols = find(lead_lag_incidence');
i_cols_A = i_cols;
if options_.terminal_condition == 1
s = eye(ny) ;
s(:,isf) = s(:,isf)+c(ic,1:nyf) ;
ic = ic + ny ;
c(ic,nrc) = s\c(ic,nrc) ;
c = bksup1(c,ny,nrc,iyf,options_.periods) ;
c = reshape(c,ny,options_.periods+1) ;
oo_.endo_simul(:,it_init+(0:options_.periods)) = oo_.endo_simul(:,it_init+(0:options_.periods))+options_.slowc*c ;
else
c = bksup1(c,ny,nrc,iyf,options_.periods) ;
c = reshape(c,ny,options_.periods) ;
oo_.endo_simul(:,it_init+(0:options_.periods-1)) = oo_.endo_simul(:,it_init+(0:options_.periods-1))+options_.slowc*c ;
for it = 2:(periods+1)
[d1,jacobian] = model_dynamic(Y(i_cols),exo_simul, params, ...
steady_state,it);
if it == 2
A(i_rows,i_cols_A1) = jacobian(:,i_cols_1);
elseif it == periods+1
A(i_rows,i_cols_A(i_cols_T)) = jacobian(:,i_cols_T);
else
A(i_rows,i_cols_A) = jacobian(:,i_cols_j);
end
res(i_rows) = d1;
i_rows = i_rows + ny;
i_cols = i_cols + ny;
if it > 2
i_cols_A = i_cols_A + ny;
end
end
err = max(max(abs(c./options_.scalv')));
disp([num2str(iter) ' - err = ' num2str(err)]) ;
disp([' Time of iteration :' num2str(etime(clock,h2))]) ;
err = max(abs(res));
if err < options_.dynatol.f
stop = 1 ;
......@@ -112,10 +122,17 @@ for iter = 1:options_.maxit_
oo_.deterministic_simulation.status = 1;% Convergency obtained.
oo_.deterministic_simulation.error = err;
oo_.deterministic_simulation.iterations = iter;
oo_.endo_simul = reshape(Y,ny,periods+2);
break
end
dy = -A\res;
Y(i_upd) = Y(i_upd) + dy;
end
if ~stop
fprintf('\n') ;
disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
......
function sim1a
% function sim1a
% Performs deterministic simulations with lead or lag on one period
% Alternative algorithm to the one implemented in sim1
function sim1_lbj
% function sim1_lbj
% performs deterministic simulations with lead or lag on one period
% using the historical LBJ algorithm
%
% INPUTS
% ...
% OUTPUTS
% ...
% ALGORITHM
% ...
% Laffargue, Boucekkine, Juillard (LBJ)
% see Juillard (1996) Dynare: A program for the resolution and
% simulation of dynamic models with forward variables through the use
% of a relaxation algorithm. CEPREMAP. Couverture Orange. 9602.
%
% SPECIAL REQUIREMENTS
% None.
% Copyright (C) 1996-2012 Dynare Team
% Copyright (C) 1996-2010 Dynare Team
%
% This file is part of Dynare.
%
......@@ -34,19 +37,13 @@ global M_ options_ oo_
lead_lag_incidence = M_.lead_lag_incidence;
ny = M_.endo_nbr;
max_lag = M_.maximum_endo_lag;
ny = size(oo_.endo_simul,1) ;
nyp = nnz(lead_lag_incidence(1,:)) ;
iyp = find(lead_lag_incidence(1,:)>0) ;
ny0 = nnz(lead_lag_incidence(2,:)) ;
iy0 = find(lead_lag_incidence(2,:)>0) ;
nyf = nnz(lead_lag_incidence(3,:)) ;
iyf = find(lead_lag_incidence(3,:)>0) ;
nd = nyp+ny0+nyf;
nrs = ny+nyp+nyf+1 ;
nrc = nyf+1 ;
iyf = find(lead_lag_incidence(3,:)>0) ;
iyp = find(lead_lag_incidence(1,:)>0) ;
isp = [1:nyp] ;
is = [nyp+1:ny+nyp] ;
isf = iyf+nyp ;
......@@ -54,63 +51,57 @@ isf1 = [nyp+ny+1:nyf+nyp+ny+1] ;
stop = 0 ;
iz = [1:ny+nyp+nyf];
periods = options_.periods
steady_state = oo_.steady_state;
params = M_.params;
endo_simul = oo_.endo_simul;
exo_simul = oo_.exo_simul;
i_cols_1 = nonzeros(lead_lag_incidence(2:3,:)');
i_cols_A1 = find(lead_lag_incidence(2:3,:)');
i_cols_T = nonzeros(lead_lag_incidence(1:2,:)');
i_cols_j = 1:nd;
i_upd = ny+(1:periods*ny);
Y = endo_simul(:);
disp (['-----------------------------------------------------']) ;
disp (['MODEL SIMULATION :']) ;
fprintf('\n') ;
it_init = M_.maximum_lag+1 ;
model_dynamic = str2func([M_.fname,'_dynamic']);
z = Y(find(lead_lag_incidence'));
[d1,jacobian] = model_dynamic(z,oo_.exo_simul, params, ...
steady_state,2);
A = sparse([],[],[],periods*ny,periods*ny,periods*nnz(jacobian));
res = zeros(periods*ny,1);
h1 = clock ;
for iter = 1:options_.maxit_
h2 = clock ;
i_rows = 1:ny;
i_cols = find(lead_lag_incidence');
i_cols_A = i_cols;
if options_.terminal_condition == 0
c = zeros(ny*options_.periods,nrc) ;
else
c = zeros(ny*(options_.periods+1),nrc) ;
end
for it = 2:(periods+1)
[d1,jacobian] = model_dynamic(Y(i_cols),exo_simul, params, ...
steady_state,it);
if it == 2
A(i_rows,i_cols_A1) = jacobian(:,i_cols_1);
elseif it == periods+1
A(i_rows,i_cols_A(i_cols_T)) = jacobian(:,i_cols_T);
else
A(i_rows,i_cols_A) = jacobian(:,i_cols_j);
end
res(i_rows) = d1;
i_rows = i_rows + ny;
i_cols = i_cols + ny;
if it > 2
i_cols_A = i_cols_A + ny;
end
it_ = it_init ;
z = [oo_.endo_simul(iyp,it_-1) ; oo_.endo_simul(:,it_) ; oo_.endo_simul(iyf,it_+1)] ;
[d1,jacobian] = feval([M_.fname '_dynamic'],z,oo_.exo_simul, M_.params, oo_.steady_state,it_);
jacobian = [jacobian(:,iz) -d1] ;
ic = [1:ny] ;
icp = iyp ;
c (ic,:) = jacobian(:,is)\jacobian(:,isf1) ;
for it_ = it_init+(1:options_.periods-1)
z = [oo_.endo_simul(iyp,it_-1) ; oo_.endo_simul(:,it_) ; oo_.endo_simul(iyf,it_+1)] ;
[d1,jacobian] = feval([M_.fname '_dynamic'],z,oo_.exo_simul, ...
M_.params, oo_.steady_state, it_);
jacobian = [jacobian(:,iz) -d1] ;
jacobian(:,[isf nrs]) = jacobian(:,[isf nrs])-jacobian(:,isp)*c(icp,:) ;
ic = ic + ny ;
icp = icp + ny ;
c (ic,:) = jacobian(:,is)\jacobian(:,isf1) ;
end
if options_.terminal_condition == 1
s = eye(ny) ;
s(:,isf) = s(:,isf)+c(ic,1:nyf) ;
ic = ic + ny ;
c(ic,nrc) = s\c(ic,nrc) ;
c = bksup1(c,ny,nrc,iyf,options_.periods) ;
c = reshape(c,ny,options_.periods+1) ;
oo_.endo_simul(:,it_init+(0:options_.periods)) = oo_.endo_simul(:,it_init+(0:options_.periods))+options_.slowc*c ;
else
c = bksup1(c,ny,nrc,iyf,options_.periods) ;
c = reshape(c,ny,options_.periods) ;
oo_.endo_simul(:,it_init+(0:options_.periods-1)) = oo_.endo_simul(:,it_init+(0:options_.periods-1))+options_.slowc*c ;
end
err = max(abs(res));
err = max(max(abs(c./options_.scalv')));
disp([num2str(iter) ' - err = ' num2str(err)]) ;
disp([' Time of iteration :' num2str(etime(clock,h2))]) ;
if err < options_.dynatol.f
stop = 1 ;
......@@ -122,17 +113,10 @@ for iter = 1:options_.maxit_
oo_.deterministic_simulation.status = 1;% Convergency obtained.
oo_.deterministic_simulation.error = err;
oo_.deterministic_simulation.iterations = iter;
oo_.endo_simul = reshape(Y,ny,periods+2);
break
end
dy = -A\res;
Y(i_upd) = Y(i_upd) + dy;
end
if ~stop
fprintf('\n') ;
disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
......
......@@ -33,18 +33,23 @@ global M_ options_ oo_
test_for_deep_parameters_calibration(M_);
if options_.stack_solve_algo < 0 || options_.stack_solve_algo > 5
error('SIMUL: stack_solve_algo must be between 0 and 5')
if options_.stack_solve_algo < 0 || options_.stack_solve_algo > 6
error('SIMUL: stack_solve_algo must be between 0 and 6')
end
if ~options_.block && ~options_.bytecode && options_.stack_solve_algo ~= 0
error('SIMUL: you must use stack_solve_algo=0 when not using block nor bytecode option')
if ~options_.block && ~options_.bytecode && options_.stack_solve_algo ~= 0 ...
&& options_.stack_solve_algo ~= 6
error('SIMUL: you must use stack_solve_algo=0 or stack_solve_algo=6 when not using block nor bytecode option')
end
if options_.block && ~options_.bytecode && options_.stack_solve_algo == 5
error('SIMUL: you can''t use stack_solve_algo = 5 without bytecode option')
end
if (options_.block || options_.bytecode) && options_.stack_solve_algo == 6
error('SIMUL: you can''t use stack_solve_algo = 6 with block or bytecode option')
end
if exist('OCTAVE_VERSION') && options_.stack_solve_algo == 2
error('SIMUL: you can''t use stack_solve_algo = 2 under Octave')
end
......@@ -91,7 +96,11 @@ else
elseif M_.maximum_endo_lag == 0 % Purely forward model
sim1_purely_forward;
else % General case
sim1;
if options_.stack_solve_algo == 0
sim1;
else % stack_solve_algo = 6
sim1_lbj;
end
end
end;
end;
......
......@@ -78,7 +78,7 @@ for blockFlag = 0:1
default_stack_solve_algo = 0;
if ~blockFlag && ~bytecodeFlag
solve_algos = 1:4;
stack_solve_algos = 0;
stack_solve_algos = [0 6];
elseif blockFlag && ~bytecodeFlag
solve_algos = [1:4 6:8];
stack_solve_algos = 0:4;
......
......@@ -75,7 +75,7 @@ for blockFlag = 0:1
default_stack_solve_algo = 0;
if !blockFlag && !bytecodeFlag
solve_algos = 0:4;
stack_solve_algos = 0;
stack_solve_algos = [0 6];
elseif blockFlag && !bytecodeFlag
solve_algos = [0:4 6 8];
stack_solve_algos = [0 1 3 4];
......
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