@Lucifier :
yes i didnt hepified properly in my previous post intentionally. i only
purpose was to set min at position A[i+1][j] after heafiying.( rest i dint
care ) .

secondly about the complexity, what i was saying
if given array is:-

1 3 4 8 9
2 5 18 25 50
6 7 22 45 55

now comparing 3 > 2 , swap we get

1 2 4 8 9
3 5 18 25 50
6 7 22 45 55
now about heapifying the highlighted array , i was considering this
highlighted matrix
3 5 18 25 50
6 7 22 45 55

as two 1-dimensional array 3 5 18 25 50 6 7 22 45 55

now we can apply heapify procedure to this 1-D array (bcozz in m/m this 2D
array is nothing but contiguous acquired space )

PARENT = floor(*i*/2)
LEFT (*i*)  = 2i
RIGHT (*i*) = 2i + 1

is this approach is wrong ??


On Wed, Jan 11, 2012 at 10:34 PM, Lucifer <[email protected]> wrote:

> @atul..
>
> Sorry, but i don't agree with both of ur posts...
>
> First of all, the complexity won't be log(m*n) for heapifying..
> log(m*n) is valid in case of a heap represented in the form of a
> binary tree..
> But, i have have repeatedly  stressing in my previous posts that the
> submatrix heap is not a binary tree heap but rather a graph or say a
> binary tree (not really tree) where its subtrees share some nodes...
>
> Disagree with the following comment..
> /**
> it seem that the sub-matrix need to be heapifyed for A[i][j] is
> A[i+1][j] to A[row][col]
> there is no need to include A[i][j+1] to A[i][col] for A[i][j] as you
> have
> mentioned above.
> **/
>
> Also, i see that you have not properly heapified the submatrices
> correctly in the example that u have provided in the previous post..
>
> Plz go thru my last post and see if ur doubts can get clarified..
>
> ---------------------------------------------
>
> Really sorry, in case previously given details by me were
> inadequate...
> Was posting in a hurry :)...
> --------------------------------------------
> Hope, now all doubts would be cleared...
> -----------------------------------------------
>
>
> On Jan 11, 9:55 pm, Lucifer <[email protected]> wrote:
> > @Ankur..
> >
> > I will try to explain the approach with an example..
> >
> > Say the given matrix (sorted row wise and column wise) is as follows:
> >
> > a1   a2    a3     a4
> >
> > b1   b2    b3     b4
> >
> > c1   c2    c3     c4
> >
> > d1   d2    d3     d4
> >
> > Now, we want to sort the 2D array such that when all the rows are
> > aligned sequentially it should result in a sorted sequence..
> > i.e.
> >
> > F1    F2    F3    F4
> > .............
> > ................
> > F13  F14 F15  F16
> >
> > such that F1 <= F2 <=....<= F16..
> >
> > Now, let take each row at a time and ensure that that row contains all
> > the elements as expected in the output matrix..
> >
> > Row - 1 :
> > M[0][0] = a1, which is at the correct place.. hence we won't touch
> > it..
> >
> > Now our task is to pick the second smallest no. in the matrix and
> > place it at M[0][1]..
> > Currently, M[0][1] is the second smallest in Row-1, but we are not
> > sure whether its the second smallest in the entire matrix..
> > Hence, only way we can check that is to look in the submatrix (M[1][0]
> > -- M[3][3])
> >
> > Now, as we know that in the submatrix enclosed within (M[1][0] -- M[3]
> > [3]) the smallest element present in this submatrix is positioned at
> > M[1][0], therefore we will check M[0][1] against M[1][0]..
> >
> > If M[0][1] <= M[1][0],
> >       that means M[0][1] has the second smallest element in the entire
> > matrix..
> > else
> >       M[1][0] is the second smallest element in the entire matrix and
> > we will swap M[1][0] with M[0][1]..
> >
> > Now, there are few things we need ensure if we end up swapping the
> > values:
> > 1) After swapping M[0][1]'s new value will be smaller than its
> > original value, therefore the following is still valid:
> >                    M[0][1] <= M[0][2] <=M[0][3]
> >           and also as M[0][1]'s new value was previously placed below
> > M[0][0], hence it is >= M[0][0] ..
> >          that means after swapping Row-1 still mains the sorted
> > order...
> > 2)  Old value of M[1][0] <= M[1][1]..
> >   Hence, the new value of M[0][1] is still <= M[1][1]..
> >        therefore the sorted order of Column-2 is still valid...
> > 3)  Now, new value of M[1][0] >= M[0][0] as an impact of old value of
> > M[0][1] >= M[0][0]
> >               Also, new value of M[1][0] <= M[1][1] as an impact of
> > old value of M[0][1] <= M[1][1]..
> >         [ point 3 can be proved by the using the explanation from
> > points 1 &2..
> > 4) Now the only thing that we need to ensure is that Column - 1 is in
> > sorted order i.e M[1][0] (new) <= M[2][0] (old)..
> >       If the above is true that means the submatrix enclosed within
> > (M[1][0] -- M[3][3])  is stabalized and has the row/column wise sorted
> > order property in place...
> >       What if its not ?? then we need to readjust the submatrix ...
> > Once we do that we are done for the current iteration..
> >       [ we will talk abt stabalization in sometime.. lets take it for
> > granted right now..]
> >
> > Now, we will follow the same approach for M[0][2], so that  it holds
> > the third largest element..
> >
> > Once we are done with Row -1.. we have the first 4 smallest elements
> > in place and we move on to the next row and follow a same process..
> > For ex-
> > Row -2
> > M[1][0] is already in place and has the 5th largest element..
> > Hence, lets look at M[1][1].. For this we will consider the submatrix
> > at (M[2][0] -- M[3][3]) and follow the same steps as
> > above..
> >
> >
> ---------------------------------------------------------------------------
> ----------------------------------------
> > Now lets talk abt how to stabilize the submatrix when the top-left
> > corner of the submatrix is replaced with another value...
> >
> > Say the given matrix 'R' to be stabilized is:
> >
> > a   b   c
> >
> > d   e   f
> >
> > g   h   i
> >
> > Now. if 'a' replaced with 'x'...
> >
> > x   b   c
> >
> > d   e   f
> >
> > g   h   i
> >
> > while(1)
> > {
> > If x <= min (b,d ), // here b is nothing but the element placed next
> > to 'x' on the same row..
> >                           // d is the element placed right below 'x'
> > in the same column...
> >        then we are done...
> >        break;
> > else
> >    swap ('x', min (b,d))
> >
> > }
> >
> > Once, we break out of the while loop, we know that the matrix has been
> > stabilized and also R[0][0] has the smallest value..
> >
> > // Observe that either 'x' shifts to the right position or to the
> > position just below it..
> > // Hence, whats the max. no. of shifts that 'x' can have.??
> >  // no. of columns + no. of rows...
> > // Hence, heapifying time is  (no. of columns + no. of rows)
> >
> > Additional explanation:
> > Now, for matrix R[0][0], its childs when interpreted as a heap are
> > located at R[0][1] and R[1][0]...
> >
> > Now, we know for sure that, the submatrix (R[0][1]... R[M][N]) has the
> > smallest element at R[0][1]..
> > Similarly, submatrix (R[1][0]... R[M][N]) has the smallest element at
> > R[1][0]...
> >
> > If you observe closely then:
> > Elements in submatrix (R[0][0]... R[M][N])
> >               =
> >             Elements in submatrix (R[0][1]... R[M][N])
> >                                U
> >             Elements in submatrix (R[1][0]... R[M][N])
> >                                U
> >                             R[0][0]..
> >
> > Looking at the above equation we can say that, if R[0][0] has been
> > replaced and not the smallest element, then the smallest element will
> > be one of (R[1][0] or R[0][1] )..
> > And this rule applies as we keep reducing the size of the matrix.. if
> > shifts occur as explained above....
> >
> > --------------------------------------------------------
> >
> > On Jan 11, 6:10 pm, Gene <[email protected]> wrote:
> >
> >
> >
> >
> >
> >
> >
> > > Think about the cost of picking the minimum.  It's not O(1).
> >
> > > On Jan 11, 3:34 am, Sanjay Rajpal <[email protected]> wrote:
> >
> > > > How can it be mn log mn ?
> >
> > > > it will be O(mn) as we elements are sorted, we simply pick minimum
> at each
> > > > iteration of the loop. Since there are mn elements, so complexity
> will be
> > > > O(mn).
> >
> > > > Correct me if m wrong.
> > > > *
> > > > Sanjay Kumar
> > > > B.Tech Final Year
> > > > Department of Computer Engineering
> > > > National Institute of Technology Kurukshetra
> > > > Kurukshetra - 136119
> > > > Haryana, India
> > > > Contact: +91-8053566286
> > > > *
> >
> > > > On Wed, Jan 11, 2012 at 12:29 AM, Ankur Garg <[email protected]>
> wrote:
> > > > > If we use K merge I think the time complexity would be nm lognm
> >
> > > > > I think we must try doing in O(m*n)
> >
> > > > > On Wed, Jan 11, 2012 at 1:54 PM, Ankur Garg <[email protected]>
> wrote:
> >
> > > > >> @Shady Rows are already sorted ...
> >
> > > > >> On Wed, Jan 11, 2012 at 1:53 PM, shady <[email protected]> wrote:
> >
> > > > >>> ^^ true, sort the rows and then a K-way merge.
> >
> > > > >>> On Wed, Jan 11, 2012 at 1:00 PM, Sanjay Rajpal <
> [email protected]>wrote:
> >
> > > > >>>> I guess sort the array such that elements are sorted finally in
> such a
> > > > >>>> way that if we print them row by row, the result is a sorted
> array.
> >
> > > > >>>> K-way merge can be useful.
> > > > >>>> *
> > > > >>>> Sanjay Kumar
> > > > >>>> B.Tech Final Year
> > > > >>>> Department of Computer Engineering
> > > > >>>> National Institute of Technology Kurukshetra
> > > > >>>> Kurukshetra - 136119
> > > > >>>> Haryana, India
> > > > >>>> Contact: +91-8053566286
> > > > >>>> *
> >
> > > > >>>> On Tue, Jan 10, 2012 at 11:28 PM, prakash y <
> [email protected]>wrote:
> >
> > > > >>>>> "sort the whole matrix in ascending array" means?
> > > > >>>>> can you please explain ?
> >
> > > > >>>>> On Wed, Jan 11, 2012 at 12:53 PM, atul anand <
> [email protected]>wrote:
> >
> > > > >>>>>> Given 2D array.
> >
> > > > >>>>>> The rows are sorted in ascending order and the colums are
> sorted in
> > > > >>>>>> ascending order.
> >
> > > > >>>>>> We have to sort the whole matrix in ascending array.
> >
> > > > >>>>>> We cannot use extra space.
> >
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