but still the binary tree solution is of more practical use.i will
explain the solution once i reach my comp

On 4/11/10, Nikhil Agarwal <[email protected]> wrote:
>
>
> On Sun, Apr 11, 2010 at 9:56 PM, Rohit Saraf <[email protected]>
> wrote:
>>
>> Time complexity is O(n log n). But the last solution I gave has O(n).
>>
>> What did u not understand abt thesolution
>
>
> @Rohit Please explain how that Binary tree solution works.
>>
>>
>> --------------------------------------------------
>> Rohit Saraf
>> Second Year Undergraduate,
>> Dept. of Computer Science and Engineering
>> IIT Bombay
>> http://www.cse.iitb.ac.in/~rohitfeb14
>>
>>
>> On Sun, Apr 11, 2010 at 11:00 AM, Priyanka Chatterjee
>> <[email protected]> wrote:
>>>
>>>
>>>
>>> On 11 April 2010 10:46, Rohit Saraf <[email protected]> wrote:
>>>>
>>>> Construct a binary tree from the data (maintain the size of subtree
>>>> under each node).
>>>> Do rotations till the left subtree does not have size k. Rotation is a
>>>> constant time operation.
>>>> Please prove the correctness of your algorithm with the time complexity
>>>>
>>>> --------------------------------------------------
>>>> Rohit Saraf
>>>> Second Year Undergraduate,
>>>> Dept. of Computer Science and Engineering
>>>> IIT Bombay
>>>> http://www.cse.iitb.ac.in/~rohitfeb14
>>>>
>>>>
>>>>
>>>> On Mon, Mar 29, 2010 at 11:15 AM, blackDiamond <[email protected]>
>>>> wrote:
>>>>>
>>>>> nice solution appreciate it. but your algorithm is wasting time in
>>>>> finding all the element...
>>>>> instead of that just find boundary line kth element which can help as
>>>>> in finding element greater that kth and element small than kth and that
>>>>> soluton can be done in O(N)
>>>>>
>>>>>
>>>>> On Sun, Mar 28, 2010 at 10:02 PM, CHERUVU JAANU REDDY
>>>>> <[email protected]> wrote:
>>>>>>
>>>>>>
>>>>>> 1) Construct max heap by taking first k elements in an array
>>>>>> 2) if k+1 element less than root of max heap
>>>>>>        a) Delete root of max heap
>>>>>>        b) Insert k+1 element in max heap and apply heapify method
>>>>>> 3) else skip the  element
>>>>>> 4) apply above procedure for all n elements in an array
>>>>>>
>>>>>> At last you will get k smallest elements and root is kth smallest
>>>>>> element in the array
>>>>>>
>>>>>> this is O(nlogk)
>>>>>>
>>>>>>
>>>>>>
>>>>>> ----------------------------------------
>>>>>> CHERUVU JAANU REDDY
>>>>>> M.Tech in CSIS
>>>>>>
>>>>>>
>>>>>> On Sun, Mar 28, 2010 at 8:41 PM, abhijith reddy
>>>>>> <[email protected]> wrote:
>>>>>>>
>>>>>>> Can any one tell how to do this when there are 'm' queries like
>>>>>>> "query i j k" find the kth largest element in between indices i->j in
>>>>>>> an array.
>>>>>>> When m is large even an O(n) algorithm would be slow.
>>>>>>> I thinking that each query could be answered in O(sqrt(n)) time
>>>>>>> So any suggestions ?
>>>>>>>
>>>>>>> Thanks
>>>>>>>
>>>>>>>
>>>>>>> On Sun, Mar 28, 2010 at 7:57 PM, blackDiamond <[email protected]>
>>>>>>> wrote:
>>>>>>>>
>>>>>>>> there are better solution of O(n) are posted in the thread.......[?].
>>>>>>>> using order statices ....
>>>>>>>>
>>>>>>>>
>>>>>>>> On Sun, Mar 28, 2010 at 6:49 PM, Mukesh Kumar thakur
>>>>>>>> <[email protected]> wrote:
>>>>>>>>>
>>>>>>>>> Create a temp array temp[0..k-1] of size k.
>>>>>>>>> 2) Traverse the array arr[k..n-1]. While traversing, keep updating
>>>>>>>>> the smallest element of temp[]
>>>>>>>>> 3) Return the smallest of temp[]
>>>>>>>>> Time Complexity: O((n-k)*k).
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> try it ..............for this problem[?]
>>>>>>>>>
>>>>>>>>> --
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>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> --
>>>>>>>> ~~~~BL/\CK_D!AMOND~~~~~~~~
>>>>>>>>
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>>>>>>>
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>>>>>>
>>>>>>
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>>>>>
>>>>>
>>>>>
>>>>>
>>>>> --
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>>>>>
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>>>>
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>>>
>>>
>>>
>>>
>>> --
>>> Thanks & Regards,
>>> Priyanka Chatterjee
>>> Third Year Undergraduate Student,
>>> Computer Science & Engineering,
>>> National Institute Of Technology,Durgapur
>>> India
>>> http://priyanka-nit.blogspot.com/
>>>
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>>
>>
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>
>
>
>
> --
> Thanks & Regards
> Nikhil Agarwal
> Junior Undergraduate
> Computer Science & Engineering,
> National Institute Of Technology, Durgapur,India
> http://tech-nikk.blogspot.com
>
>
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>


-- 
--------------------------------------------------
Rohit Saraf
Second Year Undergraduate,
Dept. of Computer Science and Engineering
IIT Bombay
http://www.cse.iitb.ac.in/~rohitfeb14

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