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In case it saves anyone some time, I am using the following routine. Since this is for pedagogical purposes only, I restricted the matrices to rational entries. Bug reports welcome.

# Naive Gaussian reduction
def gauss_method(M,rescale_leading_entry=False):
    """Describe the reduction to echelon form of the given matrix of rationals.

    M  matrix of rationals   e.g., M = matrix(QQ, [[..], [..], ..])
    rescale_leading_entry=False  boolean  make the leading entries to 1's

    Returns: None.  Side effect: M is reduced.  Note: this is echelon form, 
    not reduced echelon form; this routine does not end the same way as does 
    M.echelon_form().

    """
    num_rows=M.nrows()
    num_cols=M.ncols()
    print M    

    col = 0   # all cols before this are already done
    for row in range(0,num_rows): 
        # ?Need to swap in a nonzero entry from below
        while (col < num_cols
               and M[row][col] == 0): 
            for i in M.nonzero_positions_in_column(col):
                if i > row:
                    print " swap row",row+1,"with row",i+1
                    M.swap_rows(row,i)
                    print M
                    break     
            else:
                col += 1

        if col >= num_cols:
            break

        # Now guaranteed M[row][col] != 0
        if (rescale_leading_entry
           and M[row][col] != 1):
            print " take",1/M[row][col],"times row",row+1
            M.rescale_row(row,1/M[row][col])
            print M
        change_flag=False
        for changed_row in range(row+1,num_rows):
            if M[changed_row][col] != 0:
                change_flag=True
                factor=-1*M[changed_row][col]/M[row][col]
                print " take",factor,"times row",row+1,"plus row",changed_row+1 
                M.add_multiple_of_row(changed_row,row,factor)
        if change_flag:
            print M
        col +=1