This file is indexed.

/usr/include/TiledArray/algebra/conjgrad.h is in libtiledarray-dev 0.4.4-1.

This file is owned by root:root, with mode 0o644.

The actual contents of the file can be viewed below.

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
/*
 *  This file is a part of TiledArray.
 *  Copyright (C) 2013  Virginia Tech
 *
 *  This program 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.
 *
 *  This program 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 this program.  If not, see <http://www.gnu.org/licenses/>.
 *
 *  Eduard Valeyev
 *  Department of Chemistry, Virginia Tech
 *
 *  conjgrad.h
 *  May 20, 2013
 *
 */

#ifndef TILEDARRAY_ALGEBRA_CONJGRAD_H__INCLUDED
#define TILEDARRAY_ALGEBRA_CONJGRAD_H__INCLUDED

#include <sstream>
#include <TiledArray/array.h>
#include <TiledArray/algebra/diis.h>
#include <TiledArray/algebra/utils.h>

namespace TiledArray {

  /// Solves linear system <tt> a(x) = b </tt> using conjugate gradient solver
  /// where \c a is a linear function of \c x .

  /// \tparam D type of \c x and \c b, as well as the preconditioner;
  /// \tparam F type that evaluates the LHS, will call \c F::operator()(x,result) ,
  /// \c D must implement <tt> operator()(const D&, D&) const </tt>
  /// \c D::element_type must be defined and \c D must provide the following
  /// stand-alone functions:
  ///   \li <tt> std::size_t size(const D&) </tt>
  ///   \li <tt> D clone(const D&) </tt>
  ///   \li <tt> D copy(const D&) </tt>
  ///   \li <tt> value_type minabs_value(const D&) </tt>
  ///   \li <tt> value_type maxabs_value(const D&) </tt>
  ///   \li <tt> void vec_multiply(D& a, const D& b) </tt> (element-wise multiply of \c a by \c b )
  ///   \li <tt> value_type dot_product(const D& a, const D& b) </tt>
  ///   \li <tt> void scale(D&, value_type) </tt>
  ///   \li <tt> void axpy(D& y, value_type a, const D& x) </tt>
  ///   \li <tt> void assign(D&, const D&) </tt>
  ///   \li <tt> double norm2(const D&) </tt>
  template <typename D, typename F>
  struct ConjugateGradientSolver {
    typedef typename D::element_type value_type;

    /// \param a object of type F
    /// \param b RHS
    /// \param x unknown
    /// \param preconditioner
    /// \param convergence_target The convergence target [default = -1.0]
    /// \return The 2-norm of the residual, a(x) - b, divided by the number of
    /// elements in the residual.
    value_type operator()(F& a, const D& b, D& x, const D& preconditioner,
        value_type convergence_target = -1.0)
    {

      std::size_t n = size(x);
      assert(n == size(preconditioner));

      const bool use_diis = false;
      DIIS<D> diis;

      // solution vector
      D XX_i;
      // residual vector
      D RR_i = clone(b);
      // preconditioned residual vector
      D ZZ_i;
      // direction vector
      D PP_i;
      D APP_i = clone(b);

      // approximate the condition number as the ratio of the min and max elements of the preconditioner
      // assuming that preconditioner is the approximate inverse of A in Ax - b =0
      const value_type precond_min = minabs_value(preconditioner);
      const value_type precond_max = maxabs_value(preconditioner);
      const value_type cond_number = precond_max / precond_min;
      //std::cout << "condition number = " << precond_max << " / " << precond_min << " = " << cond_number << std::endl;
      // if convergence target is given, estimate of how tightly the system can be converged
      if (convergence_target < 0.0) {
        convergence_target = 1e-15 * cond_number;
      }
      else { // else warn if the given system is not sufficiently well conditioned
        if (convergence_target < 1e-15 * cond_number)
          std::cout << "WARNING: ConjugateGradient convergence target (" << convergence_target
                    << ") may be too low for 64-bit precision" << std::endl;
      }

      bool converged = false;
      const unsigned int max_niter = n;
      value_type rnorm2 = 0.0;
      const std::size_t rhs_size = size(b);

      // starting guess: x_0 = D^-1 . b
      XX_i = copy(b);
      vec_multiply(XX_i, preconditioner);

      // r_0 = b - a(x)
      a(XX_i, RR_i);  // RR_i = a(XX_i)
      scale(RR_i, -1.0);
      axpy(RR_i, 1.0, b); // RR_i = b - a(XX_i)

      if (use_diis)
        diis.extrapolate(XX_i, RR_i, true);

      // z_0 = D^-1 . r_0
      ZZ_i = copy(RR_i);
      vec_multiply(ZZ_i, preconditioner);

      // p_0 = z_0
      PP_i = copy(ZZ_i);

      unsigned int iter = 0;
      while (not converged) {

        // alpha_i = (r_i . z_i) / (p_i . A . p_i)
        value_type rz_norm2 = dot_product(RR_i, ZZ_i);
        a(PP_i,APP_i);

        const value_type pAp_i = dot_product(PP_i, APP_i);
        const value_type alpha_i = rz_norm2 / pAp_i;

        // x_i += alpha_i p_i
        axpy(XX_i, alpha_i, PP_i);

        // r_i -= alpha_i Ap_i
        axpy(RR_i, -alpha_i, APP_i);

        if (use_diis)
          diis.extrapolate(XX_i, RR_i, true);

        const value_type r_ip1_norm = norm2(RR_i) / rhs_size;
        if (r_ip1_norm < convergence_target) {
          converged = true;
          rnorm2 = r_ip1_norm;
        }

        // z_i = D^-1 . r_i
        ZZ_i = copy(RR_i);
        vec_multiply(ZZ_i, preconditioner);

        const value_type rz_ip1_norm2 = dot_product(ZZ_i, RR_i);

        const value_type beta_i = rz_ip1_norm2 / rz_norm2;

        // p_i = z_i+1 + beta_i p_i
        // 1) scale p_i by beta_i
        // 2) add z_i+1 (i.e. current contents of z_i)
        scale(PP_i, beta_i);
        axpy(PP_i, 1.0, ZZ_i);

        ++iter;
        //std::cout << "iter=" << iter << " dnorm=" << r_ip1_norm << std::endl;

        if (iter >= max_niter) {
          assign(x, XX_i);
          throw std::domain_error("ConjugateGradient: max # of iterations exceeded");
        }
      } // solver loop

      assign(x, XX_i);

      return rnorm2;
    }
  };

};

#endif // TILEDARRAY_ALGEBRA_CONJGRAD_H__INCLUDED