--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/gcc/lambda-mat.c	Fri Jul 17 14:47:48 2009 +0900
@@ -0,0 +1,661 @@
+/* Integer matrix math routines
+   Copyright (C) 2003, 2004, 2005, 2007, 2008 Free Software Foundation, Inc.
+   Contributed by Daniel Berlin <dberlin@dberlin.org>.
+
+This file is part of GCC.
+
+GCC 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, or (at your option) any later
+version.
+
+GCC 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 GCC; see the file COPYING3.  If not see
+<http://www.gnu.org/licenses/>.  */
+
+#include "config.h"
+#include "system.h"
+#include "coretypes.h"
+#include "tm.h"
+#include "ggc.h"
+#include "tree.h"
+#include "tree-flow.h"
+#include "lambda.h"
+
+static void lambda_matrix_get_column (lambda_matrix, int, int, 
+				      lambda_vector);
+
+/* Allocate a matrix of M rows x  N cols.  */
+
+lambda_matrix
+lambda_matrix_new (int m, int n)
+{
+  lambda_matrix mat;
+  int i;
+
+  mat = GGC_NEWVEC (lambda_vector, m);
+  
+  for (i = 0; i < m; i++)
+    mat[i] = lambda_vector_new (n);
+
+  return mat;
+}
+
+/* Copy the elements of M x N matrix MAT1 to MAT2.  */
+
+void
+lambda_matrix_copy (lambda_matrix mat1, lambda_matrix mat2,
+		    int m, int n)
+{
+  int i;
+
+  for (i = 0; i < m; i++)
+    lambda_vector_copy (mat1[i], mat2[i], n);
+}
+
+/* Store the N x N identity matrix in MAT.  */
+
+void
+lambda_matrix_id (lambda_matrix mat, int size)
+{
+  int i, j;
+
+  for (i = 0; i < size; i++)
+    for (j = 0; j < size; j++)
+      mat[i][j] = (i == j) ? 1 : 0;
+}
+
+/* Return true if MAT is the identity matrix of SIZE */
+
+bool
+lambda_matrix_id_p (lambda_matrix mat, int size)
+{
+  int i, j;
+  for (i = 0; i < size; i++)
+    for (j = 0; j < size; j++)
+      {
+	if (i == j)
+	  {
+	    if (mat[i][j] != 1)
+	      return false;
+	  }
+	else
+	  {
+	    if (mat[i][j] != 0)
+	      return false;
+	  }
+      }
+  return true;
+}
+
+/* Negate the elements of the M x N matrix MAT1 and store it in MAT2.  */
+
+void
+lambda_matrix_negate (lambda_matrix mat1, lambda_matrix mat2, int m, int n)
+{
+  int i;
+
+  for (i = 0; i < m; i++)
+    lambda_vector_negate (mat1[i], mat2[i], n);
+}
+
+/* Take the transpose of matrix MAT1 and store it in MAT2.
+   MAT1 is an M x N matrix, so MAT2 must be N x M.  */
+
+void
+lambda_matrix_transpose (lambda_matrix mat1, lambda_matrix mat2, int m, int n)
+{
+  int i, j;
+
+  for (i = 0; i < n; i++)
+    for (j = 0; j < m; j++)
+      mat2[i][j] = mat1[j][i];
+}
+
+
+/* Add two M x N matrices together: MAT3 = MAT1+MAT2.  */
+
+void
+lambda_matrix_add (lambda_matrix mat1, lambda_matrix mat2,
+		   lambda_matrix mat3, int m, int n)
+{
+  int i;
+
+  for (i = 0; i < m; i++)
+    lambda_vector_add (mat1[i], mat2[i], mat3[i], n);
+}
+
+/* MAT3 = CONST1 * MAT1 + CONST2 * MAT2.  All matrices are M x N.  */
+
+void
+lambda_matrix_add_mc (lambda_matrix mat1, int const1,
+		      lambda_matrix mat2, int const2,
+		      lambda_matrix mat3, int m, int n)
+{
+  int i;
+
+  for (i = 0; i < m; i++)
+    lambda_vector_add_mc (mat1[i], const1, mat2[i], const2, mat3[i], n);
+}
+
+/* Multiply two matrices: MAT3 = MAT1 * MAT2.
+   MAT1 is an M x R matrix, and MAT2 is R x N.  The resulting MAT2
+   must therefore be M x N.  */
+
+void
+lambda_matrix_mult (lambda_matrix mat1, lambda_matrix mat2,
+		    lambda_matrix mat3, int m, int r, int n)
+{
+
+  int i, j, k;
+
+  for (i = 0; i < m; i++)
+    {
+      for (j = 0; j < n; j++)
+	{
+	  mat3[i][j] = 0;
+	  for (k = 0; k < r; k++)
+	    mat3[i][j] += mat1[i][k] * mat2[k][j];
+	}
+    }
+}
+
+/* Get column COL from the matrix MAT and store it in VEC.  MAT has
+   N rows, so the length of VEC must be N.  */
+
+static void
+lambda_matrix_get_column (lambda_matrix mat, int n, int col,
+			  lambda_vector vec)
+{
+  int i;
+
+  for (i = 0; i < n; i++)
+    vec[i] = mat[i][col];
+}
+
+/* Delete rows r1 to r2 (not including r2).  */
+
+void
+lambda_matrix_delete_rows (lambda_matrix mat, int rows, int from, int to)
+{
+  int i;
+  int dist;
+  dist = to - from;
+
+  for (i = to; i < rows; i++)
+    mat[i - dist] = mat[i];
+
+  for (i = rows - dist; i < rows; i++)
+    mat[i] = NULL;
+}
+
+/* Swap rows R1 and R2 in matrix MAT.  */
+
+void
+lambda_matrix_row_exchange (lambda_matrix mat, int r1, int r2)
+{
+  lambda_vector row;
+
+  row = mat[r1];
+  mat[r1] = mat[r2];
+  mat[r2] = row;
+}
+
+/* Add a multiple of row R1 of matrix MAT with N columns to row R2:
+   R2 = R2 + CONST1 * R1.  */
+
+void
+lambda_matrix_row_add (lambda_matrix mat, int n, int r1, int r2, int const1)
+{
+  int i;
+
+  if (const1 == 0)
+    return;
+
+  for (i = 0; i < n; i++)
+    mat[r2][i] += const1 * mat[r1][i];
+}
+
+/* Negate row R1 of matrix MAT which has N columns.  */
+
+void
+lambda_matrix_row_negate (lambda_matrix mat, int n, int r1)
+{
+  lambda_vector_negate (mat[r1], mat[r1], n);
+}
+
+/* Multiply row R1 of matrix MAT with N columns by CONST1.  */
+
+void
+lambda_matrix_row_mc (lambda_matrix mat, int n, int r1, int const1)
+{
+  int i;
+
+  for (i = 0; i < n; i++)
+    mat[r1][i] *= const1;
+}
+
+/* Exchange COL1 and COL2 in matrix MAT. M is the number of rows.  */
+
+void
+lambda_matrix_col_exchange (lambda_matrix mat, int m, int col1, int col2)
+{
+  int i;
+  int tmp;
+  for (i = 0; i < m; i++)
+    {
+      tmp = mat[i][col1];
+      mat[i][col1] = mat[i][col2];
+      mat[i][col2] = tmp;
+    }
+}
+
+/* Add a multiple of column C1 of matrix MAT with M rows to column C2:
+   C2 = C2 + CONST1 * C1.  */
+
+void
+lambda_matrix_col_add (lambda_matrix mat, int m, int c1, int c2, int const1)
+{
+  int i;
+
+  if (const1 == 0)
+    return;
+
+  for (i = 0; i < m; i++)
+    mat[i][c2] += const1 * mat[i][c1];
+}
+
+/* Negate column C1 of matrix MAT which has M rows.  */
+
+void
+lambda_matrix_col_negate (lambda_matrix mat, int m, int c1)
+{
+  int i;
+
+  for (i = 0; i < m; i++)
+    mat[i][c1] *= -1;
+}
+
+/* Multiply column C1 of matrix MAT with M rows by CONST1.  */
+
+void
+lambda_matrix_col_mc (lambda_matrix mat, int m, int c1, int const1)
+{
+  int i;
+
+  for (i = 0; i < m; i++)
+    mat[i][c1] *= const1;
+}
+
+/* Compute the inverse of the N x N matrix MAT and store it in INV.
+
+   We don't _really_ compute the inverse of MAT.  Instead we compute
+   det(MAT)*inv(MAT), and we return det(MAT) to the caller as the function
+   result.  This is necessary to preserve accuracy, because we are dealing
+   with integer matrices here.
+
+   The algorithm used here is a column based Gauss-Jordan elimination on MAT
+   and the identity matrix in parallel.  The inverse is the result of applying
+   the same operations on the identity matrix that reduce MAT to the identity
+   matrix.
+
+   When MAT is a 2 x 2 matrix, we don't go through the whole process, because
+   it is easily inverted by inspection and it is a very common case.  */
+
+static int lambda_matrix_inverse_hard (lambda_matrix, lambda_matrix, int);
+
+int
+lambda_matrix_inverse (lambda_matrix mat, lambda_matrix inv, int n)
+{
+  if (n == 2)
+    {
+      int a, b, c, d, det;
+      a = mat[0][0];
+      b = mat[1][0];
+      c = mat[0][1];
+      d = mat[1][1];      
+      inv[0][0] =  d;
+      inv[0][1] = -c;
+      inv[1][0] = -b;
+      inv[1][1] =  a;
+      det = (a * d - b * c);
+      if (det < 0)
+	{
+	  det *= -1;
+	  inv[0][0] *= -1;
+	  inv[1][0] *= -1;
+	  inv[0][1] *= -1;
+	  inv[1][1] *= -1;
+	}
+      return det;
+    }
+  else
+    return lambda_matrix_inverse_hard (mat, inv, n);
+}
+
+/* If MAT is not a special case, invert it the hard way.  */
+
+static int
+lambda_matrix_inverse_hard (lambda_matrix mat, lambda_matrix inv, int n)
+{
+  lambda_vector row;
+  lambda_matrix temp;
+  int i, j;
+  int determinant;
+
+  temp = lambda_matrix_new (n, n);
+  lambda_matrix_copy (mat, temp, n, n);
+  lambda_matrix_id (inv, n);
+
+  /* Reduce TEMP to a lower triangular form, applying the same operations on
+     INV which starts as the identity matrix.  N is the number of rows and
+     columns.  */
+  for (j = 0; j < n; j++)
+    {
+      row = temp[j];
+
+      /* Make every element in the current row positive.  */
+      for (i = j; i < n; i++)
+	if (row[i] < 0)
+	  {
+	    lambda_matrix_col_negate (temp, n, i);
+	    lambda_matrix_col_negate (inv, n, i);
+	  }
+
+      /* Sweep the upper triangle.  Stop when only the diagonal element in the
+	 current row is nonzero.  */
+      while (lambda_vector_first_nz (row, n, j + 1) < n)
+	{
+	  int min_col = lambda_vector_min_nz (row, n, j);
+	  lambda_matrix_col_exchange (temp, n, j, min_col);
+	  lambda_matrix_col_exchange (inv, n, j, min_col);
+
+	  for (i = j + 1; i < n; i++)
+	    {
+	      int factor;
+
+	      factor = -1 * row[i];
+	      if (row[j] != 1)
+		factor /= row[j];
+
+	      lambda_matrix_col_add (temp, n, j, i, factor);
+	      lambda_matrix_col_add (inv, n, j, i, factor);
+	    }
+	}
+    }
+
+  /* Reduce TEMP from a lower triangular to the identity matrix.  Also compute
+     the determinant, which now is simply the product of the elements on the
+     diagonal of TEMP.  If one of these elements is 0, the matrix has 0 as an
+     eigenvalue so it is singular and hence not invertible.  */
+  determinant = 1;
+  for (j = n - 1; j >= 0; j--)
+    {
+      int diagonal;
+
+      row = temp[j];
+      diagonal = row[j];
+
+      /* The matrix must not be singular.  */
+      gcc_assert (diagonal);
+
+      determinant = determinant * diagonal;
+
+      /* If the diagonal is not 1, then multiply the each row by the
+         diagonal so that the middle number is now 1, rather than a
+         rational.  */
+      if (diagonal != 1)
+	{
+	  for (i = 0; i < j; i++)
+	    lambda_matrix_col_mc (inv, n, i, diagonal);
+	  for (i = j + 1; i < n; i++)
+	    lambda_matrix_col_mc (inv, n, i, diagonal);
+
+	  row[j] = diagonal = 1;
+	}
+
+      /* Sweep the lower triangle column wise.  */
+      for (i = j - 1; i >= 0; i--)
+	{
+	  if (row[i])
+	    {
+	      int factor = -row[i];
+	      lambda_matrix_col_add (temp, n, j, i, factor);
+	      lambda_matrix_col_add (inv, n, j, i, factor);
+	    }
+
+	}
+    }
+
+  return determinant;
+}
+
+/* Decompose a N x N matrix MAT to a product of a lower triangular H
+   and a unimodular U matrix such that MAT = H.U.  N is the size of
+   the rows of MAT.  */
+
+void
+lambda_matrix_hermite (lambda_matrix mat, int n,
+		       lambda_matrix H, lambda_matrix U)
+{
+  lambda_vector row;
+  int i, j, factor, minimum_col;
+
+  lambda_matrix_copy (mat, H, n, n);
+  lambda_matrix_id (U, n);
+
+  for (j = 0; j < n; j++)
+    {
+      row = H[j];
+
+      /* Make every element of H[j][j..n] positive.  */
+      for (i = j; i < n; i++)
+	{
+	  if (row[i] < 0)
+	    {
+	      lambda_matrix_col_negate (H, n, i);
+	      lambda_vector_negate (U[i], U[i], n);
+	    }
+	}
+
+      /* Stop when only the diagonal element is nonzero.  */
+      while (lambda_vector_first_nz (row, n, j + 1) < n)
+	{
+	  minimum_col = lambda_vector_min_nz (row, n, j);
+	  lambda_matrix_col_exchange (H, n, j, minimum_col);
+	  lambda_matrix_row_exchange (U, j, minimum_col);
+
+	  for (i = j + 1; i < n; i++)
+	    {
+	      factor = row[i] / row[j];
+	      lambda_matrix_col_add (H, n, j, i, -1 * factor);
+	      lambda_matrix_row_add (U, n, i, j, factor);
+	    }
+	}
+    }
+}
+
+/* Given an M x N integer matrix A, this function determines an M x
+   M unimodular matrix U, and an M x N echelon matrix S such that
+   "U.A = S".  This decomposition is also known as "right Hermite".
+   
+   Ref: Algorithm 2.1 page 33 in "Loop Transformations for
+   Restructuring Compilers" Utpal Banerjee.  */
+
+void
+lambda_matrix_right_hermite (lambda_matrix A, int m, int n,
+			     lambda_matrix S, lambda_matrix U)
+{
+  int i, j, i0 = 0;
+
+  lambda_matrix_copy (A, S, m, n);
+  lambda_matrix_id (U, m);
+
+  for (j = 0; j < n; j++)
+    {
+      if (lambda_vector_first_nz (S[j], m, i0) < m)
+	{
+	  ++i0;
+	  for (i = m - 1; i >= i0; i--)
+	    {
+	      while (S[i][j] != 0)
+		{
+		  int sigma, factor, a, b;
+
+		  a = S[i-1][j];
+		  b = S[i][j];
+		  sigma = (a * b < 0) ? -1: 1;
+		  a = abs (a);
+		  b = abs (b);
+		  factor = sigma * (a / b);
+
+		  lambda_matrix_row_add (S, n, i, i-1, -factor);
+		  lambda_matrix_row_exchange (S, i, i-1);
+
+		  lambda_matrix_row_add (U, m, i, i-1, -factor);
+		  lambda_matrix_row_exchange (U, i, i-1);
+		}
+	    }
+	}
+    }
+}
+
+/* Given an M x N integer matrix A, this function determines an M x M
+   unimodular matrix V, and an M x N echelon matrix S such that "A =
+   V.S".  This decomposition is also known as "left Hermite".
+   
+   Ref: Algorithm 2.2 page 36 in "Loop Transformations for
+   Restructuring Compilers" Utpal Banerjee.  */
+
+void
+lambda_matrix_left_hermite (lambda_matrix A, int m, int n,
+			     lambda_matrix S, lambda_matrix V)
+{
+  int i, j, i0 = 0;
+
+  lambda_matrix_copy (A, S, m, n);
+  lambda_matrix_id (V, m);
+
+  for (j = 0; j < n; j++)
+    {
+      if (lambda_vector_first_nz (S[j], m, i0) < m)
+	{
+	  ++i0;
+	  for (i = m - 1; i >= i0; i--)
+	    {
+	      while (S[i][j] != 0)
+		{
+		  int sigma, factor, a, b;
+
+		  a = S[i-1][j];
+		  b = S[i][j];
+		  sigma = (a * b < 0) ? -1: 1;
+		  a = abs (a);
+      b = abs (b);
+		  factor = sigma * (a / b);
+
+		  lambda_matrix_row_add (S, n, i, i-1, -factor);
+		  lambda_matrix_row_exchange (S, i, i-1);
+
+		  lambda_matrix_col_add (V, m, i-1, i, factor);
+		  lambda_matrix_col_exchange (V, m, i, i-1);
+		}
+	    }
+	}
+    }
+}
+
+/* When it exists, return the first nonzero row in MAT after row
+   STARTROW.  Otherwise return rowsize.  */
+
+int
+lambda_matrix_first_nz_vec (lambda_matrix mat, int rowsize, int colsize,
+			    int startrow)
+{
+  int j;
+  bool found = false;
+
+  for (j = startrow; (j < rowsize) && !found; j++)
+    {
+      if ((mat[j] != NULL)
+	  && (lambda_vector_first_nz (mat[j], colsize, startrow) < colsize))
+	found = true;
+    }
+
+  if (found)
+    return j - 1;
+  return rowsize;
+}
+
+/* Calculate the projection of E sub k to the null space of B.  */
+
+void
+lambda_matrix_project_to_null (lambda_matrix B, int rowsize,
+			       int colsize, int k, lambda_vector x)
+{
+  lambda_matrix M1, M2, M3, I;
+  int determinant;
+
+  /* Compute c(I-B^T inv(B B^T) B) e sub k.  */
+
+  /* M1 is the transpose of B.  */
+  M1 = lambda_matrix_new (colsize, colsize);
+  lambda_matrix_transpose (B, M1, rowsize, colsize);
+
+  /* M2 = B * B^T */
+  M2 = lambda_matrix_new (colsize, colsize);
+  lambda_matrix_mult (B, M1, M2, rowsize, colsize, rowsize);
+
+  /* M3 = inv(M2) */
+  M3 = lambda_matrix_new (colsize, colsize);
+  determinant = lambda_matrix_inverse (M2, M3, rowsize);
+
+  /* M2 = B^T (inv(B B^T)) */
+  lambda_matrix_mult (M1, M3, M2, colsize, rowsize, rowsize);
+
+  /* M1 = B^T (inv(B B^T)) B */
+  lambda_matrix_mult (M2, B, M1, colsize, rowsize, colsize);
+  lambda_matrix_negate (M1, M1, colsize, colsize);
+
+  I = lambda_matrix_new (colsize, colsize);
+  lambda_matrix_id (I, colsize);
+
+  lambda_matrix_add_mc (I, determinant, M1, 1, M2, colsize, colsize);
+
+  lambda_matrix_get_column (M2, colsize, k - 1, x);
+
+}
+
+/* Multiply a vector VEC by a matrix MAT.
+   MAT is an M*N matrix, and VEC is a vector with length N.  The result
+   is stored in DEST which must be a vector of length M.  */
+
+void
+lambda_matrix_vector_mult (lambda_matrix matrix, int m, int n,
+			   lambda_vector vec, lambda_vector dest)
+{
+  int i, j;
+
+  lambda_vector_clear (dest, m);
+  for (i = 0; i < m; i++)
+    for (j = 0; j < n; j++)
+      dest[i] += matrix[i][j] * vec[j];
+}
+
+/* Print out an M x N matrix MAT to OUTFILE.  */
+
+void
+print_lambda_matrix (FILE * outfile, lambda_matrix matrix, int m, int n)
+{
+  int i;
+
+  for (i = 0; i < m; i++)
+    print_lambda_vector (outfile, matrix[i], n);
+  fprintf (outfile, "\n");
+}
+