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What is the time taken to insert an element after an element pointed by some pointer?
Constant time.
What is the time complexity of accessing an element of an N dimensional array?
n
How do you select pivot in quick sort?
quick sort has a best case time complexity of O(nlogn) and worst case time complexity of 0(n^2). the best case occurs when the pivot element choosen as the center or close to the center element of the list.the time complexity can be derived for this case as: t(n)=2*t(n/2)+n. whereas the worst case time complexity for quick sort happens when the pivot element is towards the end of the list.the time complexity for this can be derived using the recurrence eqn: t(n)=t(n-1)+n
Difference between Fibonacci heap and binomial heap?
Both Binomial Heap and Fibonacci Heap are types of priority queues, but they have some differences in their structure and performance characteristics. Here's a comparison between the two: Structure: Binomial Heap: Binomial Heap is a collection of Binomial Trees. A Binomial Tree is a specific type of tree with a recursive structure. Each Binomial Tree in a Binomial Heap has a root node and may have children, where each child is also a root of a Binomial Tree of smaller size. Fibonacci Heap: Fibonacci Heap is a collection of trees, similar to Binomial Heap, but with more flexible tree structures. It allows nodes to have any number of children, not just two as in the Binomial Heap. The trees in a Fibonacci Heap are not strictly binomial trees. Operations Complexity: Binomial Heap: Binomial Heap supports the following operations with the given time complexities (n is the number of elements in the heap): Insertion: O(log n) Find minimum: O(log n) Union (merge): O(log n) Decrease key: O(log n) Deletion (extract minimum): O(log n) Fibonacci Heap: Fibonacci Heap generally has better time complexities for most operations (amortized time complexity). The amortized analysis takes into account the combined cost of a sequence of operations. For Fibonacci Heap (n is the number of elements in the heap): Insertion: O(1) Find minimum: O(1) Union (merge): O(1) Decrease key: O(1) Deletion (extract minimum): O(log n) Potential Advantage: Fibonacci Heap: The main advantage of Fibonacci Heap is that it allows constant-time insertion, decrease key, and deletion operations in the amortized sense. This makes it particularly useful in certain algorithms, such as Dijkstra's algorithm for finding the shortest path in a graph, where these operations are frequently used. Space Complexity: Binomial Heap: Binomial Heap usually requires more memory due to the strict structure of Binomial Trees. Fibonacci Heap: Fibonacci Heap can have better space complexity due to its more flexible structure, but this can vary depending on the specific implementation. Real-world Use: Binomial Heap: Binomial Heap is simpler to implement and may be preferred when ease of implementation is a concern. Fibonacci Heap: Fibonacci Heap's advantage in amortized time complexity makes it a better choice in scenarios where frequent insertions, deletions, and decrease key operations are expected. In summary, Binomial Heap and Fibonacci Heap are both priority queue data structures, but Fibonacci Heap offers better amortized time complexity for certain operations. However, Fibonacci Heap can be more complex to implement and may require more memory than Binomial Heap in some cases. The choice between the two depends on the specific use case and the performance requirements of the application.
What time required to insert element in stack with linked implementation?
O(1)
What is the time taken to insert an element after an element pointed by some pointer?
Constant time.
What is the time complexity of accessing an element of an N dimensional array?
n
How do you select pivot in quick sort?
quick sort has a best case time complexity of O(nlogn) and worst case time complexity of 0(n^2). the best case occurs when the pivot element choosen as the center or close to the center element of the list.the time complexity can be derived for this case as: t(n)=2*t(n/2)+n. whereas the worst case time complexity for quick sort happens when the pivot element is towards the end of the list.the time complexity for this can be derived using the recurrence eqn: t(n)=t(n-1)+n
Why you need to sort an array?
Because in any type of search the element can be found at the last position of your array so time complexity of the program is increased..so if array when sorted easily finds the element within less time complexity than before..
Time taken to insert an element after an element pointed by some pointer in data structure?
O(n)
Difference between Fibonacci heap and binomial heap?
Both Binomial Heap and Fibonacci Heap are types of priority queues, but they have some differences in their structure and performance characteristics. Here's a comparison between the two: Structure: Binomial Heap: Binomial Heap is a collection of Binomial Trees. A Binomial Tree is a specific type of tree with a recursive structure. Each Binomial Tree in a Binomial Heap has a root node and may have children, where each child is also a root of a Binomial Tree of smaller size. Fibonacci Heap: Fibonacci Heap is a collection of trees, similar to Binomial Heap, but with more flexible tree structures. It allows nodes to have any number of children, not just two as in the Binomial Heap. The trees in a Fibonacci Heap are not strictly binomial trees. Operations Complexity: Binomial Heap: Binomial Heap supports the following operations with the given time complexities (n is the number of elements in the heap): Insertion: O(log n) Find minimum: O(log n) Union (merge): O(log n) Decrease key: O(log n) Deletion (extract minimum): O(log n) Fibonacci Heap: Fibonacci Heap generally has better time complexities for most operations (amortized time complexity). The amortized analysis takes into account the combined cost of a sequence of operations. For Fibonacci Heap (n is the number of elements in the heap): Insertion: O(1) Find minimum: O(1) Union (merge): O(1) Decrease key: O(1) Deletion (extract minimum): O(log n) Potential Advantage: Fibonacci Heap: The main advantage of Fibonacci Heap is that it allows constant-time insertion, decrease key, and deletion operations in the amortized sense. This makes it particularly useful in certain algorithms, such as Dijkstra's algorithm for finding the shortest path in a graph, where these operations are frequently used. Space Complexity: Binomial Heap: Binomial Heap usually requires more memory due to the strict structure of Binomial Trees. Fibonacci Heap: Fibonacci Heap can have better space complexity due to its more flexible structure, but this can vary depending on the specific implementation. Real-world Use: Binomial Heap: Binomial Heap is simpler to implement and may be preferred when ease of implementation is a concern. Fibonacci Heap: Fibonacci Heap's advantage in amortized time complexity makes it a better choice in scenarios where frequent insertions, deletions, and decrease key operations are expected. In summary, Binomial Heap and Fibonacci Heap are both priority queue data structures, but Fibonacci Heap offers better amortized time complexity for certain operations. However, Fibonacci Heap can be more complex to implement and may require more memory than Binomial Heap in some cases. The choice between the two depends on the specific use case and the performance requirements of the application.
What time required to insert element in stack with linked implementation?
O(1)
What is the time complexity for searching an element in an array?
If the array is unsorted, the complexity is O(n) for the worst case. Otherwise O(log n) using binary search.
What will be the difference in running time of the heap sort algorithm if you start to apply heap sort with an array elements rather than max heap elements?
The heap sort algorithm is as follows: 1. Call the build_max_heap() function. 2. Swap the first and last elements of the max heap. 3. Reduce the heap by one element (elements that follow the heap are in sorted order). 4. Call the sift_down() function. 5. Goto step 2 unless the heap has one element. The build_max_heap() function creates the max heap and takes linear time, O(n). The sift_down() function moves the first element in the heap into its correct index, thus restoring the max heap property. This takes O(log(n)) and is called n times, so takes O(n * log(n)). The complete algorithm therefore equates to O(n + n * log(n)). If you start with a max heap rather than an unsorted array, there will be no difference in the runtime because the build_max_heap() function will still take O(n) time to complete. However, the mere fact you are starting with a max heap means you must have built that heap prior to calling the heap sort algorithm, so you've actually increased the overall runtime by an extra O(n), thus taking O(2n * log(n)) in total.
Does binary search tree take much time than the list to delete or insert an element?
No. Binary search tree will take less time to delete or insert an element. While deleting from list, more time will be required to search for the element to be deleted but BST will work faster if the no. of elements are more. Plus it depends upon which element is to be deleted and where the element is to be added. But the average time to perform these operations will be less in BST as compared to lists
What is are the time complexity or space complexity of DES algorithm?
time complexity is 2^57..and space complexity is 2^(n+1).
What is best and worst case of time complexity and space complexity of insert and delete operation in singly linked list doubly linked list?
When inserting or extracting at the end of a singly-linked list or at the beginning or end of a doubly-linked list, the complexity is constant time. Inserting or extracting in the middle of a list has linear complexity, with best case O(1) when the insertion or extraction point is already known in advance and a worst case of O(n) when it is not.
