This problem challenges us to minimize the longest distance between any two gas stations after adding k
new ones. Let’s explore this in a more intuitive way for beginners.
Understanding the Problem
Imagine you’re driving along a highway and gas stations are placed at certain positions. Some parts of the highway may have long gaps between stations. If you’re allowed to place new gas stations, you would want to put them in the widest gaps to make your journey smoother by reducing the largest distance you’d need to travel without fuel.
Goal
After adding k
new gas stations, the goal is to make the largest gap as small as possible. This is not necessarily about equalizing all distances — it's about shrinking the biggest one as much as possible.
Key Observations
- The array is sorted, so we can easily compute all existing gaps (differences between consecutive stations).
- Each gap can be split into smaller segments by placing stations in between.
- To minimize the largest segment after splitting, we should distribute the
k
stations into the largest gaps.
What Does a Valid Answer Look Like?
If a number d
is a valid answer, that means we can place k
or fewer stations in such a way that no distance between any two gas stations exceeds d
. So we're searching for the smallest possible such d.
Different Scenarios
- No stations at all: If the array is empty, there's no gap to minimize. We return 0.000000.
- Only one station: Again, no gaps exist, so the max distance is 0.
- k = 0: We cannot add any station, so the answer is simply the longest existing gap.
- Enough stations: If we have many stations, we can split the longest gaps multiple times. For example, a 100-length gap can be split into 10 parts with 9 stations — making the max distance 10.
Approach to Solve
We apply binary search on the answer (the possible distance). We start with the range of [0, max gap] and repeatedly check whether it is possible to achieve a certain distance by placing ≤ k stations. If possible, we try smaller values; if not, we go higher. We stop when we’ve narrowed the answer to a small precision like 1e-6.
This technique is often called binary search with greedy checking — binary search helps us find the minimum possible value, and greedy logic verifies if our guess is feasible.
The result is a floating point number with six decimal precision representing the minimum possible maximum distance between any two stations.