annotate scripts/optimization/pqpnonneg.m @ 17281:bc924baa2c4e

doc: Add new @qcode macro for code samples which are quoted. Macro handles options ("on") or properties ("position") more elegantly than @code{"text"}. * doc/interpreter/macros.texi: Add new @qcode macro. * doc/interpreter/tips.txi: Add documentation about @qcode macro. * doc/interpreter/basics.txi, doc/interpreter/container.txi, doc/interpreter/emacs.txi, doc/interpreter/errors.txi, doc/interpreter/eval.txi, doc/interpreter/expr.txi, doc/interpreter/external.txi, doc/interpreter/func.txi, doc/interpreter/grammar.txi, doc/interpreter/image.txi, doc/interpreter/install.txi, doc/interpreter/interp.txi, doc/interpreter/io.txi, doc/interpreter/matrix.txi, doc/interpreter/numbers.txi, doc/interpreter/oop.txi, doc/interpreter/package.txi, doc/interpreter/plot.txi, doc/interpreter/quad.txi, doc/interpreter/sparse.txi, doc/interpreter/strings.txi, doc/interpreter/system.txi, doc/interpreter/vectorize.txi, libinterp/corefcn/balance.cc, libinterp/corefcn/bitfcns.cc, libinterp/corefcn/cellfun.cc, libinterp/corefcn/conv2.cc, libinterp/corefcn/data.cc, 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scripts/ui/errordlg.m, scripts/ui/helpdlg.m, scripts/ui/inputdlg.m, scripts/ui/listdlg.m, scripts/ui/msgbox.m, scripts/ui/questdlg.m, scripts/ui/warndlg.m: Use new @qcode macro.
author Rik <rik@octave.org>
date Mon, 19 Aug 2013 20:46:38 -0700
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1 ## Copyright (C) 2008-2012 Bill Denney
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2 ## Copyright (C) 2008 Jaroslav Hajek
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3 ## Copyright (C) 2009 VZLU Prague
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4 ##
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5 ## This file is part of Octave.
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6 ##
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7 ## Octave is free software; you can redistribute it and/or modify it
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8 ## under the terms of the GNU General Public License as published by
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9 ## the Free Software Foundation; either version 3 of the License, or (at
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10 ## your option) any later version.
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11 ##
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12 ## Octave is distributed in the hope that it will be useful, but
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13 ## WITHOUT ANY WARRANTY; without even the implied warranty of
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14 ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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15 ## General Public License for more details.
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16 ##
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17 ## You should have received a copy of the GNU General Public License
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18 ## along with Octave; see the file COPYING. If not, see
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19 ## <http://www.gnu.org/licenses/>.
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20
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21 ## -*- texinfo -*-
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22 ## @deftypefn {Function File} {@var{x} =} pqpnonneg (@var{c}, @var{d})
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23 ## @deftypefnx {Function File} {@var{x} =} pqpnonneg (@var{c}, @var{d}, @var{x0})
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24 ## @deftypefnx {Function File} {[@var{x}, @var{minval}] =} pqpnonneg (@dots{})
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25 ## @deftypefnx {Function File} {[@var{x}, @var{minval}, @var{exitflag}] =} pqpnonneg (@dots{})
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26 ## @deftypefnx {Function File} {[@var{x}, @var{minval}, @var{exitflag}, @var{output}] =} pqpnonneg (@dots{})
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27 ## @deftypefnx {Function File} {[@var{x}, @var{minval}, @var{exitflag}, @var{output}, @var{lambda}] =} pqpnonneg (@dots{})
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28 ## Minimize @code{1/2*x'*c*x + d'*x} subject to @code{@var{x} >= 0}. @var{c}
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29 ## and @var{d} must be real, and @var{c} must be symmetric and positive
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30 ## definite. @var{x0} is an optional initial guess for @var{x}.
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31 ##
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32 ## Outputs:
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33 ##
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34 ## @itemize @bullet
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35 ## @item minval
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36 ##
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37 ## The minimum attained model value, 1/2*xmin'*c*xmin + d'*xmin
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38 ##
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39 ## @item exitflag
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40 ##
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41 ## An indicator of convergence. 0 indicates that the iteration count
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42 ## was exceeded, and therefore convergence was not reached; >0 indicates
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43 ## that the algorithm converged. (The algorithm is stable and will
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44 ## converge given enough iterations.)
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45 ##
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46 ## @item output
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47 ##
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48 ## A structure with two fields:
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49 ##
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50 ## @itemize @bullet
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51 ## @item @qcode{"algorithm"}: The algorithm used (@qcode{"nnls"})
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52 ##
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53 ## @item @qcode{"iterations"}: The number of iterations taken.
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54 ## @end itemize
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55 ##
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56 ## @item lambda
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57 ##
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58 ## Not implemented.
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59 ## @end itemize
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60 ## @seealso{optimset, lsqnonneg, qp}
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61 ## @end deftypefn
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62
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63 ## PKG_ADD: ## Discard result to avoid polluting workspace with ans at startup.
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64 ## PKG_ADD: [~] = __all_opts__ ("pqpnonneg");
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65
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66 ## This is analogical to the lsqnonneg implementation, which is
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67 ## implemented from Lawson and Hanson's 1973 algorithm on page
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68 ## 161 of Solving Least Squares Problems.
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69 ## It shares the convergence guarantees.
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70
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71 function [x, minval, exitflag, output, lambda] = pqpnonneg (c, d, x = [], options = struct ())
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72
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73 if (nargin == 1 && ischar (c) && strcmp (c, 'defaults'))
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74 x = optimset ("MaxIter", 1e5);
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75 return
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76 endif
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77
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78 if (! (nargin >= 2 && nargin <= 4 && ismatrix (c) && ismatrix (d) && isstruct (options)))
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79 print_usage ();
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80 endif
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81
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82 ## Lawson-Hanson Step 1 (LH1): initialize the variables.
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83 m = rows (c);
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84 n = columns (c);
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85 if (m != n)
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86 error ("pqpnonneg: matrix must be square");
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87 endif
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88
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89 if (isempty (x))
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90 ## Initial guess is 0s.
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91 x = zeros (n, 1);
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92 else
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93 ## ensure nonnegative guess.
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94 x = max (x, 0);
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95 endif
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96
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97 max_iter = optimget (options, "MaxIter", 1e5);
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98
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99 ## Initialize P, according to zero pattern of x.
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100 p = find (x > 0).';
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101 ## Initialize the Cholesky factorization.
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102 r = chol (c(p, p));
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103 usechol = true;
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104
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105 iter = 0;
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106
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107 ## LH3: test for completion.
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108 while (iter < max_iter)
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109 while (iter < max_iter)
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110 iter++;
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111
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112 ## LH6: compute the positive matrix and find the min norm solution
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113 ## of the positive problem.
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114 if (usechol)
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115 xtmp = -(r \ (r' \ d(p)));
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116 else
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117 xtmp = -(c(p,p) \ d(p));
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118 endif
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119 idx = find (xtmp < 0);
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120
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121 if (isempty (idx))
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122 ## LH7: tmp solution found, iterate.
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123 x(:) = 0;
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124 x(p) = xtmp;
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125 break;
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126 else
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127 ## LH8, LH9: find the scaling factor.
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128 pidx = p(idx);
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129 sf = x(pidx)./(x(pidx) - xtmp(idx));
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130 alpha = min (sf);
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131 ## LH10: adjust X.
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132 xx = zeros (n, 1);
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133 xx(p) = xtmp;
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134 x += alpha*(xx - x);
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135 ## LH11: move from P to Z all X == 0.
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136 ## This corresponds to those indices where minimum of sf is attained.
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137 idx = idx (sf == alpha);
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138 p(idx) = [];
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139 if (usechol)
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140 ## update the Cholesky factorization.
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141 r = choldelete (r, idx);
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142 endif
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143 endif
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144 endwhile
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145
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146 ## compute the gradient.
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147 w = -(d + c*x);
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148 w(p) = [];
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149 if (! any (w > 0))
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150 if (usechol)
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151 ## verify the solution achieved using qr updating.
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152 ## in the best case, this should only take a single step.
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153 usechol = false;
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154 continue;
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155 else
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156 ## we're finished.
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157 break;
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158 endif
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159 endif
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160
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161 ## find the maximum gradient.
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162 idx = find (w == max (w));
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163 if (numel (idx) > 1)
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164 warning ("pqpnonneg:nonunique",
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165 "a non-unique solution may be returned due to equal gradients");
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166 idx = idx(1);
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167 endif
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168 ## move the index from Z to P. Keep P sorted.
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169 z = [1:n]; z(p) = [];
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170 zidx = z(idx);
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171 jdx = 1 + lookup (p, zidx);
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172 p = [p(1:jdx-1), zidx, p(jdx:end)];
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173 if (usechol)
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174 ## insert the column into the Cholesky factorization.
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175 [r, bad] = cholinsert (r, jdx, c(p,zidx));
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176 if (bad)
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177 ## If the insertion failed, we switch off updates and go on.
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178 usechol = false;
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179 endif
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180 endif
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181
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182 endwhile
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183 ## LH12: complete.
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184
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185 ## Generate the additional output arguments.
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186 if (nargout > 1)
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187 minval = 1/2*(x'*c*x) + d'*x;
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188 endif
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189 exitflag = iter;
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190 if (nargout > 2 && iter >= max_iter)
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191 exitflag = 0;
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192 endif
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193 if (nargout > 3)
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194 output = struct ("algorithm", "nnls-pqp", "iterations", iter);
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195 endif
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196 if (nargout > 4)
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197 lambda = zeros (size (x));
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198 lambda(p) = w;
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199 endif
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200
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201 endfunction
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202
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203
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204 %!test
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205 %! C = [5 2;2 2];
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206 %! d = [3; -1];
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207 %! assert (pqpnonneg (C, d), [0;0.5], 100*eps);
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208
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209 ## Test equivalence of lsq and pqp
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210 %!test
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211 %! C = rand (20, 10);
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212 %! d = rand (20, 1);
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213 %! assert (pqpnonneg (C'*C, -C'*d), lsqnonneg (C, d), 100*eps);
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214