Time Complexity
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Given an array of n integers, find the contiguous subarray of given length k that has the maximum average value. Output the maximum average value.
About this solution: The candidate's solution is correct and solves the problem. The approach is good, iterating through the array once and keeping track of the sum of the previous k elements. The time complexity is O(n) and the space complexity is O(1).
Given an unsorted array, this algorithm finds the length of the longest consecutive elements sequence.
About this solution: The candidate's solution does not demonstrate a level of completeness and does not solve the problem. The general approach is not clear.
Find the sum of the first n integers in a list.
About this solution: This solution is correct and demonstrates a level of completeness. The approach is also optimal, as it has a time complexity of O(n).
This problem asks for the sum of all elements in an array of integers. An example input is given as [1, 2, 3, 4, 5], and the expected output is 15 .
About this solution: This solution is complete and solves the problem. The approach is straightforward and easy to understand.
This problem asks you to find the largest sum of a contiguous subarray within a given array of integers. For example, given the array [-2, 1, -3, 4, - 1, 2, 1, -5, 4], the largest sum of a contiguous subarray would be 6 (4 + -1 + 2 + 1 = 6).
About this solution: The candidate's solution is correct and solves the problem. The candidate's approach is also optimal, as they only iterate through the array once.
This problem asks you to write a function that returns the sum of the elements in an array. For example, if the input array is [1, 2, 3, 4, 5], the output should be 15 (1 + 2 + 3 + 4 + 5).
About this solution: This is a good solution. The sum function is the optimal solution because it is a built-in function that is already optimized.
Given an array of integers, this algorithm finds the length of the longest increasing subsequence in the array. For example, given the input [10, 9, 2, 5, 3, 7, 101, 18], the output would be 4.
About this solution: The candidate's solution is correct and uses dynamic programming to solve the problem. However, the solution is not very efficient because it uses two for loops.
Given an array of integers, this problem aims to find the length of the longest increasing subsequence in the array. For example, given the array [10,9,2,5, 3,7,101,18], the longest increasing subsequence is [2,3,7,101] and the length is 4.
About this solution: The candidate's solution is correct and solves the problem. The approach is to use dynamic programming. The idea is to create a list of the same length as the input list. The list will contain the length of the longest increasing subsequence ending at the index of the input list. The list will be filled in a bottom-up manner. The base case is when the list is empty. The length of the longest increasing subsequence ending at the first index is 1. The length of the longest increasing subsequence ending at the second index is 1. The length of the longest increasing subsequence ending at the third index is 1. The length of the longest increasing subsequence ending at the fourth index is 2. The length of the longest increasing subsequence ending at the fifth index is 2. The length of the longest increasing subsequence ending at the sixth index is 3. The length of the longest increasing subsequence ending at the seventh index is 4. The length of the longest increasing subsequence ending at the eighth index is 4. The solution is the maximum value in the list. The time complexity is O(n^2) and the space complexity is O(n).