Merging K Sorted Linked Lists











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Specifically, how can I improve the time complexity of my algorithm (currently it is O(listLength * numberOfLists))? It only beats 5% of accepted LeetCode solutions, which surprised me.



/**
* Definition for singly-linked list.
* public class ListNode {
* int val;
* ListNode next;
* ListNode(int x) { val = x; }
* }
*/
class Solution {
private void advance(final ListNode listNodes, final int index) {
listNodes[index] = listNodes[index].next;
}

public ListNode mergeKLists(final ListNode listNodes) {
ListNode sortedListHead = null;
ListNode sortedListNode = null;

int associatedIndex;

do {
int minValue = Integer.MAX_VALUE;
associatedIndex = -1;

for (int listIndex = 0; listIndex < listNodes.length; listIndex++) {
final ListNode listNode = listNodes[listIndex];

if (listNode != null && listNode.val < minValue) {
minValue = listNode.val;
associatedIndex = listIndex;
}
}

// An associated index of -1 indicates no more values left in any of the given lists
if (associatedIndex != -1) {
if (sortedListNode == null) {
sortedListNode = new ListNode(minValue);
sortedListHead = sortedListNode;
}
else {
sortedListNode.next = new ListNode(minValue);
sortedListNode = sortedListNode.next;
}

advance(listNodes, associatedIndex);
}
}
while (associatedIndex != -1);

return sortedListHead;
}
}


Note that the Solution class in addition to ListNode is already provided, the only code that I wrote was inside mergeKLists.










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  • You correctly determined the complexity of your approach. No surprise that it fares low. Strive for O(listLength * log(numberOfLists)).
    – vnp
    Nov 24 at 6:15















up vote
0
down vote

favorite












Specifically, how can I improve the time complexity of my algorithm (currently it is O(listLength * numberOfLists))? It only beats 5% of accepted LeetCode solutions, which surprised me.



/**
* Definition for singly-linked list.
* public class ListNode {
* int val;
* ListNode next;
* ListNode(int x) { val = x; }
* }
*/
class Solution {
private void advance(final ListNode listNodes, final int index) {
listNodes[index] = listNodes[index].next;
}

public ListNode mergeKLists(final ListNode listNodes) {
ListNode sortedListHead = null;
ListNode sortedListNode = null;

int associatedIndex;

do {
int minValue = Integer.MAX_VALUE;
associatedIndex = -1;

for (int listIndex = 0; listIndex < listNodes.length; listIndex++) {
final ListNode listNode = listNodes[listIndex];

if (listNode != null && listNode.val < minValue) {
minValue = listNode.val;
associatedIndex = listIndex;
}
}

// An associated index of -1 indicates no more values left in any of the given lists
if (associatedIndex != -1) {
if (sortedListNode == null) {
sortedListNode = new ListNode(minValue);
sortedListHead = sortedListNode;
}
else {
sortedListNode.next = new ListNode(minValue);
sortedListNode = sortedListNode.next;
}

advance(listNodes, associatedIndex);
}
}
while (associatedIndex != -1);

return sortedListHead;
}
}


Note that the Solution class in addition to ListNode is already provided, the only code that I wrote was inside mergeKLists.










share|improve this question






















  • You correctly determined the complexity of your approach. No surprise that it fares low. Strive for O(listLength * log(numberOfLists)).
    – vnp
    Nov 24 at 6:15













up vote
0
down vote

favorite









up vote
0
down vote

favorite











Specifically, how can I improve the time complexity of my algorithm (currently it is O(listLength * numberOfLists))? It only beats 5% of accepted LeetCode solutions, which surprised me.



/**
* Definition for singly-linked list.
* public class ListNode {
* int val;
* ListNode next;
* ListNode(int x) { val = x; }
* }
*/
class Solution {
private void advance(final ListNode listNodes, final int index) {
listNodes[index] = listNodes[index].next;
}

public ListNode mergeKLists(final ListNode listNodes) {
ListNode sortedListHead = null;
ListNode sortedListNode = null;

int associatedIndex;

do {
int minValue = Integer.MAX_VALUE;
associatedIndex = -1;

for (int listIndex = 0; listIndex < listNodes.length; listIndex++) {
final ListNode listNode = listNodes[listIndex];

if (listNode != null && listNode.val < minValue) {
minValue = listNode.val;
associatedIndex = listIndex;
}
}

// An associated index of -1 indicates no more values left in any of the given lists
if (associatedIndex != -1) {
if (sortedListNode == null) {
sortedListNode = new ListNode(minValue);
sortedListHead = sortedListNode;
}
else {
sortedListNode.next = new ListNode(minValue);
sortedListNode = sortedListNode.next;
}

advance(listNodes, associatedIndex);
}
}
while (associatedIndex != -1);

return sortedListHead;
}
}


Note that the Solution class in addition to ListNode is already provided, the only code that I wrote was inside mergeKLists.










share|improve this question













Specifically, how can I improve the time complexity of my algorithm (currently it is O(listLength * numberOfLists))? It only beats 5% of accepted LeetCode solutions, which surprised me.



/**
* Definition for singly-linked list.
* public class ListNode {
* int val;
* ListNode next;
* ListNode(int x) { val = x; }
* }
*/
class Solution {
private void advance(final ListNode listNodes, final int index) {
listNodes[index] = listNodes[index].next;
}

public ListNode mergeKLists(final ListNode listNodes) {
ListNode sortedListHead = null;
ListNode sortedListNode = null;

int associatedIndex;

do {
int minValue = Integer.MAX_VALUE;
associatedIndex = -1;

for (int listIndex = 0; listIndex < listNodes.length; listIndex++) {
final ListNode listNode = listNodes[listIndex];

if (listNode != null && listNode.val < minValue) {
minValue = listNode.val;
associatedIndex = listIndex;
}
}

// An associated index of -1 indicates no more values left in any of the given lists
if (associatedIndex != -1) {
if (sortedListNode == null) {
sortedListNode = new ListNode(minValue);
sortedListHead = sortedListNode;
}
else {
sortedListNode.next = new ListNode(minValue);
sortedListNode = sortedListNode.next;
}

advance(listNodes, associatedIndex);
}
}
while (associatedIndex != -1);

return sortedListHead;
}
}


Note that the Solution class in addition to ListNode is already provided, the only code that I wrote was inside mergeKLists.







java algorithm linked-list






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asked Nov 24 at 0:12









Mar Dev

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  • You correctly determined the complexity of your approach. No surprise that it fares low. Strive for O(listLength * log(numberOfLists)).
    – vnp
    Nov 24 at 6:15


















  • You correctly determined the complexity of your approach. No surprise that it fares low. Strive for O(listLength * log(numberOfLists)).
    – vnp
    Nov 24 at 6:15
















You correctly determined the complexity of your approach. No surprise that it fares low. Strive for O(listLength * log(numberOfLists)).
– vnp
Nov 24 at 6:15




You correctly determined the complexity of your approach. No surprise that it fares low. Strive for O(listLength * log(numberOfLists)).
– vnp
Nov 24 at 6:15










1 Answer
1






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0
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The time complexity is O(listLength * numberOfLists^2) because the check of which node is the smallest? is looking at one element of each list every iteration (so each iteration's complexity is O(numberOfLists)), and there are listLength * numberOfLists iterations.



You can get to O(listLength * numberOfLists * log(numberOfLists)) by using a sorted list of the ListNode elements that you are checking in each iteration, instead of the unsorted array listNodes. Let's call this list sortedNodes. You can avoid checking each element of sortedNodes every iteration because you know the first one is the smallest, and once you take this first value into the merged list and advance the node - do a binary search to decide where to move the first element after it's value has changed. (or remove it if it got to a null)






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    The time complexity is O(listLength * numberOfLists^2) because the check of which node is the smallest? is looking at one element of each list every iteration (so each iteration's complexity is O(numberOfLists)), and there are listLength * numberOfLists iterations.



    You can get to O(listLength * numberOfLists * log(numberOfLists)) by using a sorted list of the ListNode elements that you are checking in each iteration, instead of the unsorted array listNodes. Let's call this list sortedNodes. You can avoid checking each element of sortedNodes every iteration because you know the first one is the smallest, and once you take this first value into the merged list and advance the node - do a binary search to decide where to move the first element after it's value has changed. (or remove it if it got to a null)






    share|improve this answer



























      up vote
      0
      down vote













      The time complexity is O(listLength * numberOfLists^2) because the check of which node is the smallest? is looking at one element of each list every iteration (so each iteration's complexity is O(numberOfLists)), and there are listLength * numberOfLists iterations.



      You can get to O(listLength * numberOfLists * log(numberOfLists)) by using a sorted list of the ListNode elements that you are checking in each iteration, instead of the unsorted array listNodes. Let's call this list sortedNodes. You can avoid checking each element of sortedNodes every iteration because you know the first one is the smallest, and once you take this first value into the merged list and advance the node - do a binary search to decide where to move the first element after it's value has changed. (or remove it if it got to a null)






      share|improve this answer

























        up vote
        0
        down vote










        up vote
        0
        down vote









        The time complexity is O(listLength * numberOfLists^2) because the check of which node is the smallest? is looking at one element of each list every iteration (so each iteration's complexity is O(numberOfLists)), and there are listLength * numberOfLists iterations.



        You can get to O(listLength * numberOfLists * log(numberOfLists)) by using a sorted list of the ListNode elements that you are checking in each iteration, instead of the unsorted array listNodes. Let's call this list sortedNodes. You can avoid checking each element of sortedNodes every iteration because you know the first one is the smallest, and once you take this first value into the merged list and advance the node - do a binary search to decide where to move the first element after it's value has changed. (or remove it if it got to a null)






        share|improve this answer














        The time complexity is O(listLength * numberOfLists^2) because the check of which node is the smallest? is looking at one element of each list every iteration (so each iteration's complexity is O(numberOfLists)), and there are listLength * numberOfLists iterations.



        You can get to O(listLength * numberOfLists * log(numberOfLists)) by using a sorted list of the ListNode elements that you are checking in each iteration, instead of the unsorted array listNodes. Let's call this list sortedNodes. You can avoid checking each element of sortedNodes every iteration because you know the first one is the smallest, and once you take this first value into the merged list and advance the node - do a binary search to decide where to move the first element after it's value has changed. (or remove it if it got to a null)







        share|improve this answer














        share|improve this answer



        share|improve this answer








        edited Nov 25 at 9:30

























        answered Nov 25 at 8:37









        potato

        20810




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