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optim: upgrade license to GPLv3+ and mention on DESCRIPTION the other package licenses
author carandraug
date Fri, 30 Mar 2012 15:14:48 +0000
parents 41f92a4ada86
children fba8cdd5f9ad
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9929:df50d0ae107f 9930:d30cfca46e8a
1 %% Copyright (C) 2010, 2011 Olaf Till 1 %% Copyright (C) 2010, 2011 Olaf Till <olaf.till@uni-jena.de>
2 %% 2 %%
3 %% This program is free software; you can redistribute it and/or modify 3 %% This program is free software; you can redistribute it and/or modify it under
4 %% it under the terms of the GNU General Public License as published by 4 %% the terms of the GNU General Public License as published by the Free Software
5 %% the Free Software Foundation; either version 2 of the License, or (at 5 %% Foundation; either version 3 of the License, or (at your option) any later
6 %% your option) any later version. 6 %% version.
7 %% 7 %%
8 %% This program is distributed in the hope that it will be useful, but 8 %% This program is distributed in the hope that it will be useful, but WITHOUT
9 %% WITHOUT ANY WARRANTY; without even the implied warranty of 9 %% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
10 %% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU 10 %% FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
11 %% General Public License for more details. 11 %% details.
12 %% 12 %%
13 %% You should have received a copy of the GNU General Public License 13 %% You should have received a copy of the GNU General Public License along with
14 %% along with this program; if not, write to the Free Software 14 %% this program; if not, see <http://www.gnu.org/licenses/>.
15 %% Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301USA 15
16 %% m = gjp (m, k[, l])
17 %%
18 %% m: matrix; k, l: row- and column-index of pivot, l defaults to k.
19 %%
20 %% Gauss-Jordon pivot as defined in Bard, Y.: Nonlinear Parameter
21 %% Estimation, p. 296, Academic Press, New York and London 1974. In
22 %% the pivot column, this seems not quite the same as the usual
23 %% Gauss-Jordan(-Clasen) pivot. Bard gives Beaton, A. E., 'The use of
24 %% special matrix operators in statistical calculus' Research Bulletin
25 %% RB-64-51 (1964), Educational Testing Service, Princeton, New Jersey
26 %% as a reference, but this article is not easily accessible. Another
27 %% reference, whose definition of gjp differs from Bards by some
28 %% signs, is Clarke, R. B., 'Algorithm AS 178: The Gauss-Jordan sweep
29 %% operator with detection of collinearity', Journal of the Royal
30 %% Statistical Society, Series C (Applied Statistics) (1982), 31(2),
31 %% 166--168.
16 32
17 function m = gjp (m, k, l) 33 function m = gjp (m, k, l)
18
19 %% m = gjp (m, k[, l])
20 %%
21 %% m: matrix; k, l: row- and column-index of pivot, l defaults to k.
22 %%
23 %% Gauss-Jordon pivot as defined in Bard, Y.: Nonlinear Parameter
24 %% Estimation, p. 296, Academic Press, New York and London 1974. In
25 %% the pivot column, this seems not quite the same as the usual
26 %% Gauss-Jordan(-Clasen) pivot. Bard gives Beaton, A. E., 'The use of
27 %% special matrix operators in statistical calculus' Research Bulletin
28 %% RB-64-51 (1964), Educational Testing Service, Princeton, New Jersey
29 %% as a reference, but this article is not easily accessible. Another
30 %% reference, whose definition of gjp differs from Bards by some
31 %% signs, is Clarke, R. B., 'Algorithm AS 178: The Gauss-Jordan sweep
32 %% operator with detection of collinearity', Journal of the Royal
33 %% Statistical Society, Series C (Applied Statistics) (1982), 31(2),
34 %% 166--168.
35 34
36 if (nargin < 3) 35 if (nargin < 3)
37 l = k; 36 l = k;
38 end 37 end
39 38
49 m([1:k-1, k+1:end], [1:l-1, l+1:end]) = ... % except pivot row and col 48 m([1:k-1, k+1:end], [1:l-1, l+1:end]) = ... % except pivot row and col
50 m([1:k-1, k+1:end], [1:l-1, l+1:end]) - ... 49 m([1:k-1, k+1:end], [1:l-1, l+1:end]) - ...
51 m([1:k-1, k+1:end], l) * m(k, [1:l-1, l+1:end]); 50 m([1:k-1, k+1:end], l) * m(k, [1:l-1, l+1:end]);
52 m([1:k-1, k+1:end], l) = - m([1:k-1, k+1:end], l) / p; % pivot column 51 m([1:k-1, k+1:end], l) = - m([1:k-1, k+1:end], l) / p; % pivot column
53 m(k, l) = 1 / p; 52 m(k, l) = 1 / p;
53 end