Lemonade Change
An easy Greedy problem included in Striver A2Z. Below: the roles whose interviews prioritise this topic, and how to practise it.
- Topic
- Greedy
- Sheets
- 1
- Core for
- 3 roles
- Platform
- LeetCode
The problem
You are working at a lemonade stand where each lemonade costs 5 dollars. Customers are standing in a queue and each buys exactly one lemonade. They pay with 5, 10, or 20 dollar bills. You start with no change. Return true if you can provide correct change to every customer.
Example 1
- Input
- bills = [5,5,5,10,20]
- Output
- true
- Why
- First three give 5 change. Fourth (10): give back a 5. Fifth (20): give back a 10 and a 5. All customers get correct change.
Example 2
- Input
- bills = [5,5,10,10,20]
- Output
- false
- Why
- After giving change for the first three and two 10 bills, you run out of 5 dollar bills to give change. Cannot provide change.
Constraints
- 1 <= bills.length <= 10^5
- bills[i] is 5, 10, or 20
How to think about it
Updated 2026-09-09A 5-dollar bill is universal change, capable of breaking both 10 and 20 dollar payments, whereas a 10-dollar bill can only help break a 20. When making fifteen dollars in change, always surrender the less flexible 10-dollar bill first to hoard your 5-dollar bills.
Approaches, worst first
Recursive search on bill combinations
time O(2^n) · space O(n)
Explore combinations of bills to give as change for every transaction. Pointless overhead because greedy bill substitution is provably optimal.
Greedy register counterWrite this one
time O(n) · space O(1)
Maintain counts of available 5s and 10s (20s can never be used as change). On receiving 10, give a 5. On receiving 20, give a 10 and a 5 if available, otherwise three 5s. If counts drop below zero, return false immediately.
Where people lose marks · 3
- Giving three 5-dollar bills when a 10 and a 5 could have been given depletes 5-dollar bills prematurely, causing later 10-dollar customers to fail.
- Tracking twenty-dollar bills in your change inventory: 20-dollar bills can never be returned as change for any lemonade purchase.
- Decrementing bill counts before verifying availability can leave negative numbers in variables if not guarded cleanly.
The theory behind it
Greedy — the ground this problem stands on. All Greedy problems
What Greedy is
A greedy algorithm makes the best-looking choice available right now, at every step, without ever looking back or second-guessing its decision. Think of a cashier making change by handing over the largest possible coin first, repeatedly, until the total is reached. Unlike dynamic programming, which saves and compares answers to multiple overlapping paths, a greedy strategy commits to one immediate option and keeps moving forward.
When to reach for it
Reach for greedy when problems ask for minimum jumps, interval scheduling, assigning resources to maximize satisfaction, or finding fractional values. Key signals include sorted orders where greedily taking the next item never hurts future options, or gas station round trips where running balances prove reachability. If you can prove that taking the immediate best choice never leaves you worse off than any alternative, greedy gives the fastest answer.
How the pattern works
Start by sorting the input to bring the most promising candidates to the front. At each position, evaluate your local rule, take the best available piece, and update your running state. The crucial mental step is proving the greedy choice property: demonstrate that picking this immediate winner cannot block a better global solution down the road. If choosing an item now forces you to reconsider past decisions when conditions change later, greedy fails and you must switch to dynamic programming instead.
What each operation costs
| Operation | Time |
|---|---|
| sort elements to enable greedy selection | O(n log n) |
| greedy single-pass scan through sorted input | O(n) |
| greedy choice using a priority queue | O(n log n) |
What usually goes wrong with Greedy
- Applying a greedy choice without proving it yields the global optimum, such as picking the largest coin first for arbitrary denominations where dynamic programming was required.
- Forgetting to sort the input before running the greedy loop, making local decisions on unordered elements that produce invalid answers.
- Picking items based on only one attribute when the optimal decision depends on a ratio or combination of multiple attributes.
Which roles need this problem
Greedy is a core topic for these 3 roles — if you're targeting one of them, this problem is early in your path, not optional.
Secondary for 4 more roles, including Site Reliability Engineer, Search Engineer, Quant Developer.
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