From f392c78644c0d1d29a0bd6215686b47526934ab5 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?S=C3=A9bastien=20Villemot?= <sebastien@dynare.org>
Date: Thu, 21 Dec 2023 16:32:22 +0100
Subject: [PATCH] Drop unused riccati_update MEX
---
meson.build | 5 --
mex/sources/riccati_update/mexFunction.f08 | 98 ----------------------
tests/riccatiupdate.m | 87 -------------------
3 files changed, 190 deletions(-)
delete mode 100644 mex/sources/riccati_update/mexFunction.f08
delete mode 100644 tests/riccatiupdate.m
diff --git a/meson.build b/meson.build
index 6855c0c08a..b0f64c0192 100644
--- a/meson.build
+++ b/meson.build
@@ -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',
diff --git a/mex/sources/riccati_update/mexFunction.f08 b/mex/sources/riccati_update/mexFunction.f08
deleted file mode 100644
index 7443a604c7..0000000000
--- a/mex/sources/riccati_update/mexFunction.f08
+++ /dev/null
@@ -1,98 +0,0 @@
-! 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
diff --git a/tests/riccatiupdate.m b/tests/riccatiupdate.m
deleted file mode 100644
index f293c2fe0a..0000000000
--- a/tests/riccatiupdate.m
+++ /dev/null
@@ -1,87 +0,0 @@
-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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