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Verified Commit f392c786 authored by Sébastien Villemot's avatar Sébastien Villemot
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Drop unused riccati_update MEX

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Stage: test
Stage: pkg
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......@@ -346,10 +346,6 @@ shared_module('logarithmic_reduction', [ 'mex/sources/logarithmic_reduction/mexF
shared_module('disclyap_fast', [ 'mex/sources/disclyap_fast/disclyap_fast.f08' ] + mex_blas_fortran_iface,
kwargs : mex_kwargs, dependencies : [ blas_dep, lapack_dep ])
# TODO: Same remark as A_times_B_kronecker_C
shared_module('riccati_update', [ 'mex/sources/riccati_update/mexFunction.f08' ] + mex_blas_fortran_iface,
kwargs : mex_kwargs, dependencies : [ blas_dep, lapack_dep ])
qmc_sequence_src = [ 'mex/sources/sobol/qmc_sequence.cc',
'mex/sources/sobol/sobol.f08' ]
# Hack for statically linking libgfortran
......@@ -1811,7 +1807,6 @@ mod_and_m_tests = [
'solver-test-functions/wood.m',] },
{ 'test' : [ 'cyclereduction.m' ] },
{ 'test' : [ 'logarithmicreduction.m' ] },
{ 'test' : [ 'riccatiupdate.m' ] },
{ 'test' : [ 'kalman/likelihood/test_kalman_mex.m' ] },
{ 'test' : [ 'contribs.m' ],
'extra' : [ 'sandbox.mod',
......
mex/sources/riccati_update/mexFunction.f08 deleted 100644 → 0
+ 0
− 98
View file @ fbb89dc1
! Copyright © 2022-2023 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 <https://www.gnu.org/licenses/>.
! Implements Ptmp = T*(P-K*Z*P)*transpose(T)+Q where
! P is the (r x r) variance-covariance matrix of the state vector
! T is the (r x r) transition matrix of the state vector
! K is the (r x n) gain matrix
! Z is the (n x r) matrix linking observable variables to state variables
! Q is the (r x r) variance-covariance matrix of innovations in the state equation
! and accounting for different properties:
! P is a (symmetric) positive semi-definite matrix
! T can be triangular
subroutine mexFunction(nlhs, plhs, nrhs, prhs) bind(c, name='mexFunction')
use matlab_mex
use blas
implicit none (type, external)
type(c_ptr), dimension(*), intent(in), target :: prhs
type(c_ptr), dimension(*), intent(out) :: plhs
integer(c_int), intent(in), value :: nlhs, nrhs
real(real64), dimension(:,:), pointer, contiguous :: P, T, K, Z, Q, Pnew
real(real64), dimension(:,:), allocatable :: tmp1, tmp2
integer :: i, n, r
character(kind=c_char, len=2) :: num2str
! 0. Checking the consistency and validity of input arguments
if (nrhs /= 5_c_int) then
call mexErrMsgTxt("Must have 5 input arguments")
end if
if (nlhs > 1_c_int) then
call mexErrMsgTxt("Too many output arguments")
end if
do i=1,5
if (.not. (c_associated(prhs(i)) .and. mxIsDouble(prhs(i)) .and. &
(.not. mxIsComplex(prhs(i))) .and. (.not. mxIsSparse(prhs(i))))) then
write (num2str,"(i2)") i
call mexErrMsgTxt("Argument " // trim(num2str) // " should be a real dense matrix")
end if
end do
r = int(mxGetM(prhs(1))) ! Number of states
n = int(mxGetN(prhs(3))) ! Number of observables
if ((r /= mxGetN(prhs(1))) & ! Number of columns of P
&.or. (r /= mxGetM(prhs(2))) & ! Number of lines of T
&.or. (r /= mxGetN(prhs(2))) & ! Number of columns of T
&.or. (r /= mxGetM(prhs(3))) & ! Number of lines of K
&.or. (n /= mxGetM(prhs(4))) & ! Number of lines of Z
&.or. (r /= mxGetN(prhs(4))) & ! Number of columns of Z
&.or. (r /= mxGetM(prhs(5))) & ! Number of lines of Q
&.or. (r /= mxGetN(prhs(5))) & ! Number of columns of Q
) then
call mexErrMsgTxt("Input dimension mismatch")
end if
! 1. Storing the relevant information in Fortran format
P(1:r,1:r) => mxGetPr(prhs(1))
T(1:r,1:r) => mxGetPr(prhs(2))
K(1:r,1:n) => mxGetPr(prhs(3))
Z(1:n,1:r) => mxGetPr(prhs(4))
Q(1:r,1:r) => mxGetPr(prhs(5))
plhs(1) = mxCreateDoubleMatrix(int(r, mwSize), int(r, mwSize), mxREAL)
Pnew(1:r, 1:r) => mxGetPr(plhs(1))
! 2. Computing the Riccati update of the P matrix
allocate(tmp1(r,r), tmp2(r,r))
! Pnew <- Q
Pnew = Q
! tmp1 <- K*Z
call matmul_add("N", "N", 1._real64, K, Z, 0._real64, tmp1)
! tmp2 <- P
tmp2 = P
! tmp2 <- tmp2 - tmp1*P
call matmul_add("N", "N", -1._real64, tmp1, P, 1._real64, tmp2)
! tmp1 <- T*tmp2
call matmul_add("N", "N", 1._real64, T, tmp2, 0._real64, tmp1)
! Pnew <- tmp1*T' + Pnew
call matmul_add("N", "T", 1._real64, tmp1, T, 1._real64, Pnew)
end subroutine mexFunction
tests/riccatiupdate.m deleted 100644 → 0
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− 87
View file @ fbb89dc1
source_dir = getenv('source_root');
addpath([source_dir filesep 'matlab']);
dynare_config;
testFailed = 0;
skipline()
disp('*** TESTING: riccatiupdate.m ***');
t0 = clock;
% Set the number of experiments for time measurement
N = 5000;
% Set the dimension of the problem to be solved.
r = 50;
n = 100;
tol = 1e-15;
% Set the input arguments
% P, Q: use the fact that for any real matrix A, A'*A is positive semidefinite
P = rand(n,r);
P = P'*P;
Q = rand(n,r);
Q = Q'*Q;
K = rand(r,n);
Z = rand(n,r);
T = rand(r,r);
% Computing an upperbound for the norm the updated variance-covariance matrix
ub = norm(T,1)^2*norm(P,1)*(1+norm(K*Z,1))+norm(Q,1);
% Weighting the P and Q matrices to keep the norm of the variance-covariance matrix below 1
P = 0.5*P/ub;
Q = 0.5*Q/ub;
% 1. Update the state vairance-covariance matrix with Matlab
tElapsed1 = 0.;
tic;
for i=1:N
Ptmp_matlab = T*(P-K*Z*P)*transpose(T)+Q;
end
tElapsed1 = toc;
disp(['Elapsed time for the Matlab Riccati update is: ' num2str(tElapsed1) ' (N=' int2str(N) ').'])
% 2. Update the state varance-covariance matrix with the mex routine
tElapsed2 = 0.;
Ptmp_fortran = P;
try
tic;
for i=1:N
Ptmp_fortran = riccati_update(P, T, K, Z, Q);
end
tElapsed2 = toc;
disp(['Elapsed time for the Fortran Riccati update is: ' num2str(tElapsed2) ' (N=' int2str(N) ').'])
R = norm(Ptmp_fortran-Ptmp_matlab,1);
if (R > tol)
testFailed = testFailed+1;
dprintf('The Fortran Riccati update is wrong')
end
catch
testFailed = testFailed+1;
dprintf('Fortran Riccati update failed')
end
% Compare the Fortran and Matlab execution time
if tElapsed1<tElapsed2
skipline()
dprintf('Matlab Riccati update is %5.2f times faster than its Fortran counterpart.', tElapsed2/tElapsed1)
skipline()
else
skipline()
dprintf('Fortran Riccati update is %5.2f times faster than its Matlab counterpart.', tElapsed1/tElapsed2)
skipline()
end
% Compare results after multiple calls
N = 50;
disp(['After 1 update using the Riccati formula, the norm-1 discrepancy is ' num2str(norm(Ptmp_fortran-Ptmp_matlab,1)) '.']);
for i=2:N
Ptmp_matlab = T*(Ptmp_matlab-K*Z*Ptmp_matlab)*transpose(T)+Q;
Ptmp_fortran = riccati_update(Ptmp_fortran, T, K, Z, Q);
disp(['After ' int2str(i) ' updates using the Riccati formula, the norm-1 discrepancy is ' num2str(norm(Ptmp_fortran-Ptmp_matlab,1)) '.'])
end
t1 = clock;
fprintf('\n*** Elapsed time (in seconds): %.1f\n\n', etime(t1, t0));
quit(testFailed > 0)
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