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+## Copyright (C) 2007-2012 Regents of the University of California
+##
+## This file is part of Octave.
+##
+## Octave 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.
+##
+## Octave 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 Octave; see the file COPYING. If not, see
+## .
+
+## -*- texinfo -*-
+## @deftypefn {Function File} {} condest (@var{A})
+## @deftypefnx {Function File} {} condest (@var{A}, @var{t})
+## @deftypefnx {Function File} {[@var{est}, @var{v}] =} condest (@dots{})
+## @deftypefnx {Function File} {[@var{est}, @var{v}] =} condest (@var{A}, @var{solve}, @var{solve_t}, @var{t})
+## @deftypefnx {Function File} {[@var{est}, @var{v}] =} condest (@var{apply}, @var{apply_t}, @var{solve}, @var{solve_t}, @var{n}, @var{t})
+##
+## Estimate the 1-norm condition number of a matrix @var{A}
+## using @var{t} test vectors using a randomized 1-norm estimator.
+## If @var{t} exceeds 5, then only 5 test vectors are used.
+##
+## If the matrix is not explicit, e.g., when estimating the condition
+## number of @var{A} given an LU@tie{}factorization, @code{condest} uses the
+## following functions:
+##
+## @table @var
+## @item apply
+## @code{A*x} for a matrix @code{x} of size @var{n} by @var{t}.
+##
+## @item apply_t
+## @code{A'*x} for a matrix @code{x} of size @var{n} by @var{t}.
+##
+## @item solve
+## @code{A \ b} for a matrix @code{b} of size @var{n} by @var{t}.
+##
+## @item solve_t
+## @code{A' \ b} for a matrix @code{b} of size @var{n} by @var{t}.
+## @end table
+##
+## The implicit version requires an explicit dimension @var{n}.
+##
+## @code{condest} uses a randomized algorithm to approximate
+## the 1-norms.
+##
+## @code{condest} returns the 1-norm condition estimate @var{est} and
+## a vector @var{v} satisfying @code{norm (A*v, 1) == norm (A, 1) * norm
+## (@var{v}, 1) / @var{est}}. When @var{est} is large, @var{v} is an
+## approximate null vector.
+##
+## References:
+## @itemize
+## @item
+## N.J. Higham and F. Tisseur, @cite{A Block Algorithm
+## for Matrix 1-Norm Estimation, with an Application to 1-Norm
+## Pseudospectra}. SIMAX vol 21, no 4, pp 1185-1201.
+## @url{http://dx.doi.org/10.1137/S0895479899356080}
+##
+## @item
+## N.J. Higham and F. Tisseur, @cite{A Block Algorithm
+## for Matrix 1-Norm Estimation, with an Application to 1-Norm
+## Pseudospectra}. @url{http://citeseer.ist.psu.edu/223007.html}
+## @end itemize
+##
+## @seealso{cond, norm, onenormest}
+## @end deftypefn
+
+## Code originally licensed under
+##
+## Copyright (c) 2007, Regents of the University of California
+## All rights reserved.
+##
+## Redistribution and use in source and binary forms, with or without
+## modification, are permitted provided that the following conditions
+## are met:
+##
+## * Redistributions of source code must retain the above copyright
+## notice, this list of conditions and the following disclaimer.
+##
+## * Redistributions in binary form must reproduce the above
+## copyright notice, this list of conditions and the following
+## disclaimer in the documentation and/or other materials provided
+## with the distribution.
+##
+## * Neither the name of the University of California, Berkeley nor
+## the names of its contributors may be used to endorse or promote
+## products derived from this software without specific prior
+## written permission.
+##
+## THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS ``AS IS''
+## AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
+## TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
+## PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS AND
+## CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+## SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+## LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF
+## USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+## ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+## OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT
+## OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
+## SUCH DAMAGE.
+
+## Author: Jason Riedy
+## Keywords: linear-algebra norm estimation
+## Version: 0.2
+
+function [est, v] = condest (varargin)
+
+ if (nargin < 1 || nargin > 6)
+ print_usage ();
+ endif
+
+ default_t = 5;
+
+ have_A = false;
+ have_t = false;
+ have_solve = false;
+
+ if (ismatrix (varargin{1}))
+ A = varargin{1};
+ if (! issquare (A))
+ error ("condest: matrix must be square");
+ endif
+ n = rows (A);
+ have_A = true;
+
+ if (nargin > 1)
+ if (isscalar (varargin{2}))
+ t = varargin{2};
+ have_t = true;
+ elseif (nargin > 2)
+ solve = varargin{2};
+ solve_t = varargin{3};
+ have_solve = true;
+ if (nargin > 3)
+ t = varargin{4};
+ have_t = true;
+ endif
+ else
+ error ("condest: must supply both SOLVE and SOLVE_T");
+ endif
+ endif
+ elseif (nargin > 4)
+ apply = varargin{1};
+ apply_t = varargin{2};
+ solve = varargin{3};
+ solve_t = varargin{4};
+ have_solve = true;
+ n = varargin{5};
+ if (! isscalar (n))
+ error ("condest: dimension argument of implicit form must be scalar");
+ endif
+ if (nargin > 5)
+ t = varargin{6};
+ have_t = true;
+ endif
+ else
+ error ("condest: implicit form of condest requires at least 5 arguments");
+ endif
+
+ if (! have_t)
+ t = min (n, default_t);
+ endif
+
+ if (! have_solve)
+ if (issparse (A))
+ [L, U, P, Pc] = lu (A);
+ solve = @(x) Pc' * (U \ (L \ (P * x)));
+ solve_t = @(x) P' * (L' \ (U' \ (Pc * x)));
+ else
+ [L, U, P] = lu (A);
+ solve = @(x) U \ (L \ (P*x));
+ solve_t = @(x) P' * (L' \ (U' \ x));
+ endif
+ endif
+
+ if (have_A)
+ Anorm = norm (A, 1);
+ else
+ Anorm = onenormest (apply, apply_t, n, t);
+ endif
+
+ [Ainv_norm, v, w] = onenormest (solve, solve_t, n, t);
+
+ est = Anorm * Ainv_norm;
+ v = w / norm (w, 1);
+
+endfunction
+
+%!demo
+%! N = 100;
+%! A = randn (N) + eye (N);
+%! condest (A)
+%! [L,U,P] = lu (A);
+%! condest (A, @(x) U\ (L\ (P*x)), @(x) P'*(L'\ (U'\x)))
+%! condest (@(x) A*x, @(x) A'*x, @(x) U\ (L\ (P*x)), @(x) P'*(L'\ (U'\x)), N)
+%! norm (inv (A), 1) * norm (A, 1)
+
+## Yes, these test bounds are really loose. There's
+## enough randomization to trigger odd cases with hilb().
+
+%!test
+%! N = 6;
+%! A = hilb (N);
+%! cA = condest (A);
+%! cA_test = norm (inv (A), 1) * norm (A, 1);
+%! assert (cA, cA_test, -2^-8);
+
+%!test
+%! N = 6;
+%! A = hilb (N);
+%! solve = @(x) A\x; solve_t = @(x) A'\x;
+%! cA = condest (A, solve, solve_t);
+%! cA_test = norm (inv (A), 1) * norm (A, 1);
+%! assert (cA, cA_test, -2^-8);
+
+%!test
+%! N = 6;
+%! A = hilb (N);
+%! apply = @(x) A*x; apply_t = @(x) A'*x;
+%! solve = @(x) A\x; solve_t = @(x) A'\x;
+%! cA = condest (apply, apply_t, solve, solve_t, N);
+%! cA_test = norm (inv (A), 1) * norm (A, 1);
+%! assert (cA, cA_test, -2^-6);
+
+%!test
+%! N = 12;
+%! A = hilb (N);
+%! [rcondA, v] = condest (A);
+%! x = A*v;
+%! assert (norm(x, inf), 0, eps);