Greedy Algorithm Solves Jump Game II in Linear Time, Beating Dynamic Programming

Jump Game II is a classic coding problem where the goal is to find the minimum number of jumps needed to reach the last index of an array, given each element's maximum jump length. A naive dynamic programming approach solves it in O(n²) time by evaluating every possible path. A greedy algorithm offers a more efficient solution by tracking the farthest reachable index at each step and incrementing the jump count only when the current range is exhausted. This single linear pass requires no recursion or memoization, reducing time complexity significantly. A key implementation pitfall is iterating only up to the second-to-last index to avoid counting an unnecessary extra jump.