Find the Longest Palindromic Substring

Use Kadane's Algorithm to find the maximum sum. Initialize the maximum sum and current sum as the first element, then iterate through the array updating these values. For example, for [1, -2, 3, 4, -1], the maximum subarray sum is 7.

Use a sliding window and hash maps to compare character counts. Slide the window across the string and check if the counts match. For example, in 'cbaebabacd' with pattern 'abc', the start indices of anagrams are 0 and 6.

Use a dynamic programming approach to build a table that tracks palindromic substrings. For example, in 'babad', the longest palindromic substring is 'bab' or 'aba'.

DFS explores as far as possible along each branch before backtracking. Use a stack or recursion to keep track of nodes. For example, in a graph with nodes 1 -> 2 -> 3, DFS from 1 explores 1, 2, and then 3.

Use Depth-First Search with a tracking mechanism for visited nodes. If a node is revisited during the same DFS traversal, a cycle exists. For example, in a graph with edges 1 -> 2 -> 3 -> 1, a cycle is detected.

Use a min-heap of size K to keep track of the K largest elements. For each element, if it is larger than the smallest element in the heap, replace the smallest. For example, in [3, 2, 1, 5, 6, 4], the 2nd largest element is 5.

Use BFS to explore the shortest path in an unweighted graph. Enqueue the starting node, then visit each neighbor while updating distances. For example, in a graph with edges (1 -> 2), (2 -> 3), (1 -> 3), the shortest path from 1 to 3 is 1 -> 3.

A priority queue can be implemented using a heap where the highest (or lowest) priority element is always at the top. Operations include insert and extract-max (or extract-min). For example, in a max-heap, inserting 5 and 10 results in [10, 5].

BFS explores all neighbors of a node before moving to the next level. Use a queue to keep track of nodes. For example, in a graph with nodes 1 -> 2 -> 3, BFS from 1 explores 1, then 2 and 3.

Use dynamic programming to build a table where each cell represents the length of the longest common subsequence up to those indices. For example, for 'abcde' and 'aceb', the longest common subsequence is 'ace' with length 3.

Use topological sorting to order nodes and then apply dynamic programming to find the longest path. For example, in a DAG with edges 1 -> 2, 1 -> 3, 2 -> 4, 3 -> 4, the longest path is 1 -> 2 -> 4 or 1 -> 3 -> 4.

Use a hash map to count the frequency of each character, then iterate through the string to find the first character with a count of 1. For example, in 'swiss', the first non-repeating character is 'w'.

Binary search divides the search interval in half. Start with the middle element; if it matches the target, return it. Otherwise, adjust the search range to either the left or right half based on comparison. For example, searching for 4 in [1, 2, 3, 4, 5] returns index 3.

Use two pointers to traverse the linked lists. When one pointer reaches the end, move it to the start of the other list. Continue until both pointers meet. For example, if lists intersect at node with value 8, both pointers will eventually reach this node.