Furthest Building You Can Reach - Solution & Explanation

This approach uses a greedy strategy combined with a max-heap (priority queue) to determine the optimal use of bricks and ladders. The idea is to always use bricks first for the smallest height difference and use ladders for the largest, potentially reserving the ladders for future taller buildings. Whenever we encounter a new jump (difference in height), we add it to the heap, and if the total number of jumps exceeds the available ladders, we use bricks for the smallest jump. If we don’t have enough bricks, we can’t proceed further.

This C code uses a simple array to store differences between consecutive buildings that require resource expenditure. It sorts these differences in descending order and tries to accommodate the largest jumps with ladders, using bricks for the rest. While sorting and processing the differences, it tracks when there are insufficient bricks to continue, returning the last accessible building index.

This approach employs binary search to find the furthest building we can reach. The binary search is performed over the indices of the buildings (0 to n-1). For each index midpoint in the search, we check if it's possible to reach that building, given the constraints of available bricks and ladders using a helper function that checks feasibility by simulating resource allocation greedily. The search continually refines the possible maximum index based on resource ability.

In this C code, a binary search is conducted over the range of buildings. For each midpoint, a helper function 'canReach' checks the viability of reaching that point by exploiting a simulated min-heap strategy. This allows optimal usage of the available bricks and ladders, guiding the binary search to converge on the maximum reachable building index.