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////////////////////////////////////////////////////////////////////////
//
// Copyright (C) 2007-2021 The Octave Project Developers
//
// See the file COPYRIGHT.md in the top-level directory of this
// distribution or <https://octave.org/copyright/>.
//
// 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
// <https://www.gnu.org/licenses/>.
//
////////////////////////////////////////////////////////////////////////
/*
An implementation of the Reverse Cuthill-McKee algorithm (symrcm)
The implementation of this algorithm is based in the descriptions found in
@INPROCEEDINGS{,
author = {E. Cuthill and J. McKee},
title = {Reducing the Bandwidth of Sparse Symmetric Matrices},
booktitle = {Proceedings of the 24th ACM National Conference},
publisher = {Brandon Press},
pages = {157 -- 172},
location = {New Jersey},
year = {1969}
}
@BOOK{,
author = {Alan George and Joseph W. H. Liu},
title = {Computer Solution of Large Sparse Positive Definite Systems},
publisher = {Prentice Hall Series in Computational Mathematics},
ISBN = {0-13-165274-5},
year = {1981}
}
The algorithm represents a heuristic approach to the NP-complete minimum
bandwidth problem.
Written by Michael Weitzel <michael.weitzel@@uni-siegen.de>
<weitzel@@ldknet.org>
*/
#if defined (HAVE_CONFIG_H)
# include "config.h"
#endif
#include <algorithm>
#include "CSparse.h"
#include "boolNDArray.h"
#include "dNDArray.h"
#include "dSparse.h"
#include "oct-locbuf.h"
#include "oct-sparse.h"
#include "quit.h"
#include "defun.h"
#include "errwarn.h"
#include "ov.h"
#include "ovl.h"
// A node struct for the Cuthill-McKee algorithm
struct CMK_Node
{
// the node's id (matrix row index)
octave_idx_type id;
// the node's degree
octave_idx_type deg;
// minimal distance to the root of the spanning tree
octave_idx_type dist;
};
// A simple queue.
// Queues Q have a fixed maximum size N (rows,cols of the matrix) and are
// stored in an array. qh and qt point to queue head and tail.
// Enqueue operation (adds a node "o" at the tail)
inline static void
Q_enq (CMK_Node *Q, octave_idx_type N, octave_idx_type& qt, const CMK_Node& o)
{
Q[qt] = o;
qt = (qt + 1) % (N + 1);
}
// Dequeue operation (removes a node from the head)
inline static CMK_Node
Q_deq (CMK_Node * Q, octave_idx_type N, octave_idx_type& qh)
{
CMK_Node r = Q[qh];
qh = (qh + 1) % (N + 1);
return r;
}
// Predicate (queue empty)
#define Q_empty(Q, N, qh, qt) ((qh) == (qt))
// A simple, array-based binary heap (used as a priority queue for nodes)
// the left descendant of entry i
#define LEFT(i) (((i) << 1) + 1) // = (2*(i)+1)
// the right descendant of entry i
#define RIGHT(i) (((i) << 1) + 2) // = (2*(i)+2)
// the parent of entry i
#define PARENT(i) (((i) - 1) >> 1) // = floor(((i)-1)/2)
// Builds a min-heap (the root contains the smallest element). A is an array
// with the graph's nodes, i is a starting position, size is the length of A.
static void
H_heapify_min (CMK_Node *A, octave_idx_type i, octave_idx_type size)
{
octave_idx_type j = i;
for (;;)
{
octave_idx_type l = LEFT(j);
octave_idx_type r = RIGHT(j);
octave_idx_type smallest;
if (l < size && A[l].deg < A[j].deg)
smallest = l;
else
smallest = j;
if (r < size && A[r].deg < A[smallest].deg)
smallest = r;
if (smallest != j)
{
std::swap (A[j], A[smallest]);
j = smallest;
}
else
break;
}
}
// Heap operation insert. Running time is O(log(n))
static void
H_insert (CMK_Node *H, octave_idx_type& h, const CMK_Node& o)
{
octave_idx_type i = h++;
H[i] = o;
if (i == 0)
return;
do
{
octave_idx_type p = PARENT(i);
if (H[i].deg < H[p].deg)
{
std::swap (H[i], H[p]);
i = p;
}
else
break;
}
while (i > 0);
}
// Heap operation remove-min. Removes the smallest element in O(1) and
// reorganizes the heap optionally in O(log(n))
inline static CMK_Node
H_remove_min (CMK_Node *H, octave_idx_type& h, int reorg/*=1*/)
{
CMK_Node r = H[0];
H[0] = H[--h];
if (reorg)
H_heapify_min (H, 0, h);
return r;
}
// Predicate (heap empty)
#define H_empty(H, h) ((h) == 0)
// Helper function for the Cuthill-McKee algorithm. Tries to determine a
// pseudo-peripheral node of the graph as starting node.
static octave_idx_type
find_starting_node (octave_idx_type N, const octave_idx_type *ridx,
const octave_idx_type *cidx, const octave_idx_type *ridx2,
const octave_idx_type *cidx2, octave_idx_type *D,
octave_idx_type start)
{
CMK_Node w;
OCTAVE_LOCAL_BUFFER (CMK_Node, Q, N+1);
boolNDArray btmp (dim_vector (1, N), false);
bool *visit = btmp.fortran_vec ();
octave_idx_type qh = 0;
octave_idx_type qt = 0;
CMK_Node x;
x.id = start;
x.deg = D[start];
x.dist = 0;
Q_enq (Q, N, qt, x);
visit[start] = true;
// distance level
octave_idx_type level = 0;
// current largest "eccentricity"
octave_idx_type max_dist = 0;
for (;;)
{
while (! Q_empty (Q, N, qh, qt))
{
CMK_Node v = Q_deq (Q, N, qh);
if (v.dist > x.dist || (v.id != x.id && v.deg > x.deg))
x = v;
octave_idx_type i = v.id;
// add all unvisited neighbors to the queue
octave_idx_type j1 = cidx[i];
octave_idx_type j2 = cidx2[i];
while (j1 < cidx[i+1] || j2 < cidx2[i+1])
{
octave_quit ();
if (j1 == cidx[i+1])
{
octave_idx_type r2 = ridx2[j2++];
if (! visit[r2])
{
// the distance of node j is dist(i)+1
w.id = r2;
w.deg = D[r2];
w.dist = v.dist+1;
Q_enq (Q, N, qt, w);
visit[r2] = true;
if (w.dist > level)
level = w.dist;
}
}
else if (j2 == cidx2[i+1])
{
octave_idx_type r1 = ridx[j1++];
if (! visit[r1])
{
// the distance of node j is dist(i)+1
w.id = r1;
w.deg = D[r1];
w.dist = v.dist+1;
Q_enq (Q, N, qt, w);
visit[r1] = true;
if (w.dist > level)
level = w.dist;
}
}
else
{
octave_idx_type r1 = ridx[j1];
octave_idx_type r2 = ridx2[j2];
if (r1 <= r2)
{
if (! visit[r1])
{
w.id = r1;
w.deg = D[r1];
w.dist = v.dist+1;
Q_enq (Q, N, qt, w);
visit[r1] = true;
if (w.dist > level)
level = w.dist;
}
j1++;
if (r1 == r2)
j2++;
}
else
{
if (! visit[r2])
{
w.id = r2;
w.deg = D[r2];
w.dist = v.dist+1;
Q_enq (Q, N, qt, w);
visit[r2] = true;
if (w.dist > level)
level = w.dist;
}
j2++;
}
}
}
} // finish of BFS
if (max_dist < x.dist)
{
max_dist = x.dist;
for (octave_idx_type i = 0; i < N; i++)
visit[i] = false;
visit[x.id] = true;
x.dist = 0;
qt = qh = 0;
Q_enq (Q, N, qt, x);
}
else
break;
}
return x.id;
}
// Calculates the node's degrees. This means counting the nonzero elements
// in the symmetric matrix' rows. This works for non-symmetric matrices
// as well.
static octave_idx_type
calc_degrees (octave_idx_type N, const octave_idx_type *ridx,
const octave_idx_type *cidx, octave_idx_type *D)
{
octave_idx_type max_deg = 0;
for (octave_idx_type i = 0; i < N; i++)
D[i] = 0;
for (octave_idx_type j = 0; j < N; j++)
{
for (octave_idx_type i = cidx[j]; i < cidx[j+1]; i++)
{
octave_quit ();
octave_idx_type k = ridx[i];
// there is a nonzero element (k,j)
D[k]++;
if (D[k] > max_deg)
max_deg = D[k];
// if there is no element (j,k) there is one in
// the symmetric matrix:
if (k != j)
{
bool found = false;
for (octave_idx_type l = cidx[k]; l < cidx[k + 1]; l++)
{
octave_quit ();
if (ridx[l] == j)
{
found = true;
break;
}
else if (ridx[l] > j)
break;
}
if (! found)
{
// A(j,k) == 0
D[j]++;
if (D[j] > max_deg)
max_deg = D[j];
}
}
}
}
return max_deg;
}
// Transpose of the structure of a square sparse matrix
static void
transpose (octave_idx_type N, const octave_idx_type *ridx,
const octave_idx_type *cidx, octave_idx_type *ridx2,
octave_idx_type *cidx2)
{
octave_idx_type nz = cidx[N];
OCTAVE_LOCAL_BUFFER (octave_idx_type, w, N + 1);
for (octave_idx_type i = 0; i < N; i++)
w[i] = 0;
for (octave_idx_type i = 0; i < nz; i++)
w[ridx[i]]++;
nz = 0;
for (octave_idx_type i = 0; i < N; i++)
{
octave_quit ();
cidx2[i] = nz;
nz += w[i];
w[i] = cidx2[i];
}
cidx2[N] = nz;
w[N] = nz;
for (octave_idx_type j = 0; j < N; j++)
for (octave_idx_type k = cidx[j]; k < cidx[j + 1]; k++)
{
octave_quit ();
octave_idx_type q = w[ridx[k]]++;
ridx2[q] = j;
}
}
// An implementation of the Cuthill-McKee algorithm.
DEFUN (symrcm, args, ,
doc: /* -*- texinfo -*-
@deftypefn {} {@var{p} =} symrcm (@var{S})
Return the symmetric reverse @nospell{Cuthill-McKee} permutation of @var{S}.
@var{p} is a permutation vector such that
@code{@var{S}(@var{p}, @var{p})} tends to have its diagonal elements closer
to the diagonal than @var{S}. This is a good preordering for LU or
Cholesky@tie{}factorization of matrices that come from ``long, skinny''
problems. It works for both symmetric and asymmetric @var{S}.
The algorithm represents a heuristic approach to the NP-complete bandwidth
minimization problem. The implementation is based in the descriptions found
in
@nospell{E. Cuthill, J. McKee}.
@cite{Reducing the Bandwidth of Sparse Symmetric Matrices}.
Proceedings of the 24th @nospell{ACM} National Conference,
157--172 1969, Brandon Press, New Jersey.
@nospell{A. George, J.W.H. Liu}. @cite{Computer Solution of Large Sparse
Positive Definite Systems}, Prentice Hall Series in Computational
Mathematics, ISBN 0-13-165274-5, 1981.
@seealso{colperm, colamd, symamd}
@end deftypefn */)
{
if (args.length () != 1)
print_usage ();
octave_value arg = args(0);
// the parameter of the matrix is converted into a sparse matrix
//(if necessary)
octave_idx_type *cidx;
octave_idx_type *ridx;
SparseMatrix Ar;
SparseComplexMatrix Ac;
if (arg.isreal ())
{
Ar = arg.sparse_matrix_value ();
// Note cidx/ridx are const, so use xridx and xcidx...
cidx = Ar.xcidx ();
ridx = Ar.xridx ();
}
else
{
Ac = arg.sparse_complex_matrix_value ();
cidx = Ac.xcidx ();
ridx = Ac.xridx ();
}
octave_idx_type nr = arg.rows ();
octave_idx_type nc = arg.columns ();
if (nr != nc)
err_square_matrix_required ("symrcm", "S");
if (nr == 0 && nc == 0)
return ovl (NDArray (dim_vector (1, 0)));
// sizes of the heaps
octave_idx_type s = 0;
// head- and tail-indices for the queue
octave_idx_type qt = 0;
octave_idx_type qh = 0;
CMK_Node v, w;
// dimension of the matrix
octave_idx_type N = nr;
OCTAVE_LOCAL_BUFFER (octave_idx_type, cidx2, N + 1);
OCTAVE_LOCAL_BUFFER (octave_idx_type, ridx2, cidx[N]);
transpose (N, ridx, cidx, ridx2, cidx2);
// the permutation vector
NDArray P (dim_vector (1, N));
// compute the node degrees
OCTAVE_LOCAL_BUFFER (octave_idx_type, D, N);
octave_idx_type max_deg = calc_degrees (N, ridx, cidx, D);
// if none of the nodes has a degree > 0 (a matrix of zeros)
// the return value corresponds to the identity permutation
if (max_deg == 0)
{
for (octave_idx_type i = 0; i < N; i++)
P(i) = i;
return ovl (P);
}
// a heap for the a node's neighbors. The number of neighbors is
// limited by the maximum degree max_deg:
OCTAVE_LOCAL_BUFFER (CMK_Node, S, max_deg);
// a queue for the BFS. The array is always one element larger than
// the number of entries that are stored.
OCTAVE_LOCAL_BUFFER (CMK_Node, Q, N+1);
// a counter (for building the permutation)
octave_idx_type c = -1;
// upper bound for the bandwidth (=quality of solution)
// initialize the bandwidth of the graph with 0. B contains the
// the maximum of the theoretical lower limits of the subgraphs
// bandwidths.
octave_idx_type B = 0;
// mark all nodes as unvisited; with the exception of the nodes
// that have degree==0 and build a CC of the graph.
boolNDArray btmp (dim_vector (1, N), false);
bool *visit = btmp.fortran_vec ();
do
{
// locate an unvisited starting node of the graph
octave_idx_type i;
for (i = 0; i < N; i++)
if (! visit[i])
break;
// locate a probably better starting node
v.id = find_starting_node (N, ridx, cidx, ridx2, cidx2, D, i);
// mark the node as visited and enqueue it (a starting node
// for the BFS). Since the node will be a root of a spanning
// tree, its dist is 0.
v.deg = D[v.id];
v.dist = 0;
visit[v.id] = true;
Q_enq (Q, N, qt, v);
// lower bound for the bandwidth of a subgraph
// keep a "level" in the spanning tree (= min. distance to the
// root) for determining the bandwidth of the computed
// permutation P
octave_idx_type Bsub = 0;
// min. dist. to the root is 0
octave_idx_type level = 0;
// the root is the first/only node on level 0
octave_idx_type level_N = 1;
while (! Q_empty (Q, N, qh, qt))
{
v = Q_deq (Q, N, qh);
i = v.id;
c++;
// for computing the inverse permutation P where
// A(inv(P),inv(P)) or P'*A*P is banded
// P(i) = c;
// for computing permutation P where
// A(P(i),P(j)) or P*A*P' is banded
P(c) = i;
// put all unvisited neighbors j of node i on the heap
s = 0;
octave_idx_type j1 = cidx[i];
octave_idx_type j2 = cidx2[i];
octave_quit ();
while (j1 < cidx[i+1] || j2 < cidx2[i+1])
{
octave_quit ();
if (j1 == cidx[i+1])
{
octave_idx_type r2 = ridx2[j2++];
if (! visit[r2])
{
// the distance of node j is dist(i)+1
w.id = r2;
w.deg = D[r2];
w.dist = v.dist+1;
H_insert (S, s, w);
visit[r2] = true;
}
}
else if (j2 == cidx2[i+1])
{
octave_idx_type r1 = ridx[j1++];
if (! visit[r1])
{
w.id = r1;
w.deg = D[r1];
w.dist = v.dist+1;
H_insert (S, s, w);
visit[r1] = true;
}
}
else
{
octave_idx_type r1 = ridx[j1];
octave_idx_type r2 = ridx2[j2];
if (r1 <= r2)
{
if (! visit[r1])
{
w.id = r1;
w.deg = D[r1];
w.dist = v.dist+1;
H_insert (S, s, w);
visit[r1] = true;
}
j1++;
if (r1 == r2)
j2++;
}
else
{
if (! visit[r2])
{
w.id = r2;
w.deg = D[r2];
w.dist = v.dist+1;
H_insert (S, s, w);
visit[r2] = true;
}
j2++;
}
}
}
// add the neighbors to the queue (sorted by node degree)
while (! H_empty (S, s))
{
octave_quit ();
// locate a neighbor of i with minimal degree in O(log(N))
v = H_remove_min (S, s, 1);
// entered the BFS a new level?
if (v.dist > level)
{
// adjustment of bandwidth:
// "[...] the minimum bandwidth that
// can be obtained [...] is the
// maximum number of nodes per level"
if (Bsub < level_N)
Bsub = level_N;
level = v.dist;
// v is the first node on the new level
level_N = 1;
}
else
{
// there is no new level but another node on
// this level:
level_N++;
}
// enqueue v in O(1)
Q_enq (Q, N, qt, v);
}
// synchronize the bandwidth with level_N once again:
if (Bsub < level_N)
Bsub = level_N;
}
// finish of BFS. If there are still unvisited nodes in the graph
// then it is split into CCs. The computed bandwidth is the maximum
// of all subgraphs. Update:
if (Bsub > B)
B = Bsub;
}
// are there any nodes left?
while (c+1 < N);
// compute the reverse-ordering
s = N / 2 - 1;
for (octave_idx_type i = 0, j = N - 1; i <= s; i++, j--)
std::swap (P.elem (i), P.elem (j));
// increment all indices, since Octave is not C
return ovl (P+1);
}
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