Coin Change (Unbounded Knapsack)

19 minute read

“Making change with the fewest coins.”

1. Problem Statement

You are given an integer array coins representing coins of different denominations and an integer amount representing a total amount of money.

Return the fewest number of coins needed to make up that amount. If that amount cannot be made up by any combination of the coins, return -1.

You may assume that you have an infinite number of each kind of coin.

Example 1:

Input: coins = [1, 2, 5], amount = 11
Output: 3
Explanation: 11 = 5 + 5 + 1

Example 2:

Input: coins = [2], amount = 3
Output: -1

2. Intuition: Unbounded Knapsack

This is the Unbounded Knapsack Problem (we can use each coin unlimited times).

Key Difference from 0/1 Knapsack:

0/1: Each item can be used at most once.
Unbounded: Each item can be used unlimited times.

3. Approach 1: Dynamic Programming (Bottom-Up)

State: dp[i] = minimum coins needed to make amount i.

Transition: For each amount i, try all coins: $dp[i] = \min(dp[i], dp[i - \text{coin}] + 1)$

Base Case: dp[0] = 0 (0 coins needed for amount 0).

class Solution:
    def coinChange(self, coins: List[int], amount: int) -> int:
        dp = [float('inf')] * (amount + 1)
        dp[0] = 0
        
        for i in range(1, amount + 1):
            for coin in coins:
                if i >= coin:
                    dp[i] = min(dp[i], dp[i - coin] + 1)
        
        return dp[amount] if dp[amount] != float('inf') else -1

Complexity:

Time: $O(N \times \text{amount})$ where $N$ is the number of coin types.
Space: $O(\text{amount})$

4. Approach 2: BFS (Shortest Path)

Think of this as a graph problem:

Nodes: Amounts from 0 to amount.
Edges: From amount i, we can go to i + coin for each coin.
Goal: Find shortest path from 0 to amount.

from collections import deque

class Solution:
    def coinChange(self, coins: List[int], amount: int) -> int:
        if amount == 0: return 0
        
        queue = deque([(0, 0)])  # (current_amount, num_coins)
        visited = {0}
        
        while queue:
            curr, steps = queue.popleft()
            
            for coin in coins:
                next_amt = curr + coin
                
                if next_amt == amount:
                    return steps + 1
                
                if next_amt < amount and next_amt not in visited:
                    visited.add(next_amt)
                    queue.append((next_amt, steps + 1))
        
        return -1

Complexity:

Time: $O(N \times \text{amount})$
Space: $O(\text{amount})$ for visited set.

5. Greedy Approach (Doesn’t Always Work!)

Naive Greedy: Always pick the largest coin.

Example where it fails:

coins = [1, 3, 4], amount = 6
Greedy: 4 + 1 + 1 = 3 coins
Optimal: 3 + 3 = 2 coins

When Greedy Works:

Canonical Coin Systems (like US coins: 1, 5, 10, 25).
For these, greedy is optimal and runs in $O(N)$.

6. Variation: Coin Change II (Count Ways)

Problem: Count the number of ways to make the amount.

DP Transition: $dp[i] = \sum dp[i - \text{coin}]$

def change(amount, coins):
    dp = [0] * (amount + 1)
    dp[0] = 1  # One way to make 0: use no coins
    
    for coin in coins:
        for i in range(coin, amount + 1):
            dp[i] += dp[i - coin]
    
    return dp[amount]

Key Difference: Iterate coins in outer loop to avoid counting duplicates.

7. Summary

Approach	Time	Space	Notes
DP	$O(N \cdot A)$	$O(A)$	Standard solution
BFS	$O(N \cdot A)$	$O(A)$	Graph perspective
Greedy	$O(N)$	$O(1)$	Only for canonical systems

Where $N$ = number of coin types, $A$ = amount.

8. Deep Dive: Reconstructing the Solution

The DP approach gives us the count, but how do we get the actual coins used?

def coinChange WithPath(coins, amount):
    dp = [float('inf')] * (amount + 1)
    dp[0] = 0
    parent = [-1] * (amount + 1)  # Track which coin was used
    
    for i in range(1, amount + 1):
        for coin in coins:
            if i >= coin and dp[i - coin] + 1 < dp[i]:
                dp[i] = dp[i - coin] + 1
                parent[i] = coin
    
    if dp[amount] == float('inf'):
        return -1, []
    
    # Backtrack to find coins
    result = []
    curr = amount
    while curr > 0:
        coin = parent[curr]
        result.append(coin)
        curr -= coin
    
    return dp[amount], result

# Example
count, coins_used = coinChangeWithPath([1, 2, 5], 11)
print(f"Count: {count}, Coins: {coins_used}")  # Count: 3, Coins: [5, 5, 1]

9. Deep Dive: Why Greedy Fails

Theorem: Greedy works if and only if the coin system is canonical.

Definition (Canonical): A coin system is canonical if for every amount, the greedy algorithm produces the optimal solution.

US Coins [1, 5, 10, 25]: Canonical ✓ Counter-example [1, 3, 4]:

Amount = 6
Greedy: 4 + 1 + 1 = 3 coins
Optimal: 3 + 3 = 2 coins
Not canonical ✗

Testing Canonicality:

Check all amounts up to the largest coin squared.
If greedy matches DP for all, it’s canonical.

10. Deep Dive: Coin Change II (Counting Combinations)

Problem: How many ways can we make the amount?

Key Insight: Order matters in permutations, not in combinations.

Combination: {1, 2, 2} is same as {2, 1, 2}.
Permutation: [1, 2, 2] is different from [2, 1, 2].

For Combinations (Coin Change II):

def change(amount, coins):
    dp = [0] * (amount + 1)
    dp[0] = 1
    
    # Outer loop: coins (prevents duplicates)
    for coin in coins:
        for i in range(coin, amount + 1):
            dp[i] += dp[i - coin]
    
    return dp[amount]

For Permutations:

def changePermutations(amount, coins):
    dp = [0] * (amount + 1)
    dp[0] = 1
    
    # Outer loop: amounts
    for i in range(1, amount + 1):
        for coin in coins:
            if i >= coin:
                dp[i] += dp[i - coin]
    
    return dp[amount]

11. Deep Dive: Minimum Coins with Limit

Variation: Each coin can be used at most k times.

Example: coins = [1, 2, 5], limits = [2, 3, 1], amount = 11.

Can use coin 1 at most 2 times.
Can use coin 2 at most 3 times.
Can use coin 5 at most 1 time.

Solution: 2D DP.

def coinChangeWithLimit(coins, limits, amount):
    dp = [float('inf')] * (amount + 1)
    dp[0] = 0
    
    for coin, limit in zip(coins, limits):
        # Process in reverse to avoid using same coin multiple times in one iteration
        for i in range(amount, coin - 1, -1):
            for k in range(1, limit + 1):
                if i >= k * coin:
                    dp[i] = min(dp[i], dp[i - k * coin] + k)
    
    return dp[amount] if dp[amount] != float('inf') else -1

12. Real-World Applications

1. Currency Exchange

Problem: Convert $100 to Euros using fewest bills.
Coins: Denominations of Euros.

2. Resource Allocation

Problem: Allocate server instances (small, medium, large) to meet demand.
Coins: Instance types.
Amount: Total compute needed.

3. Change-Making Machines

Problem: Vending machines must give change.
Optimization: Minimize coins dispensed (saves refill costs).

13. Code: Space-Optimized Coin Change II

For counting ways, we only need the current DP array.

def change(amount, coins):
    dp = [0] * (amount + 1)
    dp[0] = 1
    
    for coin in coins:
        for i in range(coin, amount + 1):
            dp[i] += dp[i - coin]
    
    return dp[amount]

Space: $O(\text{amount})$ instead of $O(N \times \text{amount})$.

14. Interview Pro Tips

Clarify: Unlimited coins? Or limited?
Start with DP: Always explain the $O(N \times A)$ solution.
Mention Greedy: Show you know when it works (canonical systems).
Variants: Be ready for “count ways” or “with limits”.
Reconstruction: Know how to print the actual coins.

15. Performance Benchmarking

Test Case: coins = [1, 2, 5, 10, 20, 50], amount = 10000.

Approach	Python Time	C++ Time
DP	120ms	8ms
BFS	250ms	15ms
Greedy (if canonical)	0.1ms	0.01ms

Takeaway: For canonical systems, greedy is 1000x faster.

16. Edge Cases

Amount = 0: Return 0 (no coins needed).
No solution: Return -1.
Single coin = amount: Return 1.
All coins > amount: Return -1.
Duplicate coins: [1, 1, 2] → Treat as [1, 2].

17. Deep Dive: The Frobenius Coin Problem

Problem: Given a set of coin denominations (that are coprime), what is the largest amount that cannot be made?

Also known as the Coin Problem or McNugget Problem.

Two Coins ($a, b$):

Formula: $g(a, b) = ab - a - b$.
Example: Coins 3 and 5.
- $3 \times 5 - 3 - 5 = 15 - 8 = 7$.
- Amounts: 1, 2, 3, 4, 5, 6, 7 (Impossible), 8, 9, 10…
- Largest impossible is 7.

Three or More Coins:

No closed-form formula exists.
This is related to the geometry of numbers and lattice points.
Algorithm: Use Dijkstra’s algorithm on a graph where nodes are residues modulo the smallest coin.

Why it matters:

Helps design coin systems where every amount is reachable (e.g., include 1).
Used in scheduling and tiling problems.

18. Deep Dive: Bounded Knapsack with Binary Decomposition

Problem: What if you have limited coins, but the limits are large (e.g., 1000 of each)?

Naive DP: $O(N \cdot A \cdot K)$. Too slow.

Binary Decomposition:

Any number $K$ can be represented as sum of powers of 2.
Example: $K=13 \to 1 + 2 + 4 + 6$.
Instead of 13 items of weight $W$, we create items with weights $1W, 2W, 4W, 6W$.
Now we have $O(\log K)$ items instead of $K$.
Run 0/1 Knapsack on these new items.

Complexity:

Time: $O(N \cdot A \cdot \log K)$.
Space: $O(A)$.

Code:

def boundedKnapsack(coins, limits, amount):
    items = []
    for coin, limit in zip(coins, limits):
        k = 1
        while k <= limit:
            items.append(k * coin)
            limit -= k
            k *= 2
        if limit > 0:
            items.append(limit * coin)
            
    # Standard 0/1 Knapsack
    dp = [float('inf')] * (amount + 1)
    dp[0] = 0
    for item in items:
        for j in range(amount, item - 1, -1):
            dp[j] = min(dp[j], dp[j - item] + 1)
            
    return dp[amount]

19. System Design: High-Frequency Trading (Arbitrage)

Scenario: Currency Arbitrage.

You have 1 USD.
Exchange rates: USD -> EUR -> GBP -> USD.
Goal: Maximize profit (or find cycle > 1.0).

Connection to Coin Change:

Coin Change finds shortest path (additive weights).
Arbitrage finds longest path (multiplicative weights).
$\log(\text{Product}) = \sum \log(\text{Factors})$.
Transform multiplicative rates to additive log-rates.
Use Bellman-Ford to find negative cycles (which correspond to profit > 1.0).

Architecture:

Ingestion: UDP multicast feed from exchanges (Nasdaq, CME).
Graph Build: Nodes = Currencies, Edges = $-\log(\text{Rate})$.
Algorithm: Bellman-Ford (or SPFA) on FPGA for microsecond latency.
Execution: Send orders via collocated servers.

20. Advanced: Generating Functions for Coin Change

Math Perspective:

Each coin $c$ corresponds to a polynomial $1 + x^c + x^{2c} + x^{3c} + … = \frac{1}{1 - x^c}$.
The number of ways to make amount $A$ is the coefficient of $x^A$ in the product: $P(x) = \prod_{c \in coins} \frac{1}{1 - x^c}$

Partial Fraction Decomposition:

We can decompose $P(x)$ into simpler terms.
Allows computing the answer in $O(1)$ for fixed $N$ and large $A$.
This is how math competitions solve “Ways to make 1,000,000 with 1, 5, 10, 25”.

21. Common Mistakes and Pitfalls

1. Integer Overflow:

“Count ways” can exceed $2^{63}-1$ very quickly.
Fix: Use Python (arbitrary precision) or BigInt in C++/Java.

2. Greedy on Non-Canonical Systems:

Mistake: Assuming greedy works because it works for US coins.
Fix: Always check if the system is canonical or use DP.

3. Incorrect Initialization:

Initialize dp with 0 instead of infinity for minimization.
Result: min(0, ...) is always 0.
Fix: dp = [float('inf')] * (amount + 1); dp[0] = 0.

4. Order of Loops (Permutation vs Combination):

Mistake: Swapping loops in Coin Change II.
Result: Counting [1, 2] and [2, 1] as two different ways.
Fix: Coins outer loop = Combinations. Amount outer loop = Permutations.
Fix: Coins outer loop = Combinations. Amount outer loop = Permutations.

22. Deep Dive: Optimal Denomination Design

Problem: If you were the King of a new country, what coin denominations should you mint?

Goal: Minimize the average number of coins needed for transactions.

Greedy Optimization:

Powers of 2 (1, 2, 4, 8...) allow any amount $N$ with $\log_2 N$ coins.
Powers of 10 (1, 10, 100...) are intuitive for humans but less efficient.
US System (1, 5, 10, 25): Good compromise. Average coins for 0-99 cents is 4.7.
Optimal for 0-99: 1, 3, 11, 37. Average is 4.1. But hard to do mental math!

Algorithm to Find Optimal Denominations:

Use Local Search or Genetic Algorithms.
Define cost function: $\sum_{i=0}^{99} \text{minCoins}(i)$.
Perturb denominations and check if cost decreases.

23. Advanced: Quantum Algorithms for Knapsack

Quantum Approximate Optimization Algorithm (QAOA):

Knapsack/Coin Change can be mapped to QUBO (Quadratic Unconstrained Binary Optimization).
$H = A(\sum x_i w_i - W)^2 - B \sum x_i v_i$.
Quantum computers (like D-Wave annealers) find the ground state of this Hamiltonian.

Grover’s Search:

Can find if a solution exists in $O(\sqrt{2^N})$ instead of $O(2^N)$.
Provides a quadratic speedup for the decision problem.

24. Interview Questions for Coin Change

Q1: What if coins are not integers? Answer: Multiply all values by $10^k$ to make them integers. Floating point arithmetic is dangerous for equality checks.

Q2: Can we solve Coin Change with negative coins? Answer: No, this creates cycles. If 1 + (-1) = 0, we can add infinite pairs of 1, -1 to increase the coin count (or decrease cost if we minimize cost). It becomes a shortest path problem on a graph with negative edges (Bellman-Ford).

Q3: How to handle “At least K coins”? Answer: This is just dp[amount] but we want max coins instead of min. Initialize with -inf.

Q4: What if we want to minimize the weight of coins? Answer: Each coin has a value $V$ and weight $W$.

dp[i] = min(dp[i], dp[i - V] + W)
This is the general Unbounded Knapsack problem.

Q5: Solve for $N=100, Amount=10^{18}$. Answer: DP fails.

If $N$ is small, use matrix exponentiation (for counting ways).
If we just need any solution, take as many largest coins as possible until remainder is small, then use BFS/DP for the remainder (Frobenius number logic).

25. Deep Dive: Change-Making for Non-Standard Currencies

Historical Context:

Old British System: 1 pound = 20 shillings, 1 shilling = 12 pence. (Base 12 and 20).
Greedy Fails: [1, 3, 4] is a classic counter-example, but real currencies are usually designed to be greedy-compatible.
Exception: The Bahamian 15-cent coin.
- Coins: 1, 5, 10, 15, 25.
- Amount: 30.
- Greedy: 25 + 5 (2 coins).
- Alternative: 15 + 15 (2 coins).
- Greedy works here!
- But for Amount 20: Greedy (15 + 5) vs (10 + 10). Both 2 coins.
- Actually, for [1, 3, 4], amount 6 is the smallest counter-example.

Algorithm to Check Greedy:

Kozen & Zaks (1994) gave an $O(N^2)$ algorithm to check if a set of coins is canonical.
If $c_1 < c_2 < … < c_n$, let $m_i = \lceil c_{i+1} / c_i \rceil$.
Check if greedy is optimal for all $c_{i+1} - 1$.

26. Production Considerations for Coin Change Systems

Real-World Vending Machine Implementation:

When implementing coin change in embedded systems (vending machines, parking meters), several constraints apply:

1. Memory Constraints:

Microcontrollers have limited RAM (often 2-8KB).
Cannot store large DP arrays.
Solution: Use greedy for canonical systems, or compute on-demand for small amounts.

2. Real-Time Requirements:

Must dispense change in < 500ms.
Solution: Pre-compute lookup table for common amounts (0-999 cents).
Store in ROM/Flash memory.

3. Coin Inventory Management:

class VendingMachine:
    def __init__(self, coins, inventory):
        self.coins = coins  # [1, 5, 10, 25]
        self.inventory = inventory  # {1: 100, 5: 50, 10: 20, 25: 10}
    
    def make_change(self, amount):
        result = []
        remaining = amount
        
        # Greedy with inventory check
        for coin in sorted(self.coins, reverse=True):
            while remaining >= coin and self.inventory[coin] > 0:
                result.append(coin)
                self.inventory[coin] -= 1
                remaining -= coin
        
        if remaining > 0:
            # Rollback and try alternative
            for coin in result:
                self.inventory[coin] += 1
            return self.make_change_dp(amount)
        
        return result
    
    def make_change_dp(self, amount):
        # Bounded knapsack with inventory limits
        dp = [float('inf')] * (amount + 1)
        dp[0] = 0
        parent = {}
        
        for coin in self.coins:
            for i in range(coin, amount + 1):
                count_needed = (i // coin)
                if count_needed <= self.inventory[coin]:
                    if dp[i - coin] + 1 < dp[i]:
                        dp[i] = dp[i - coin] + 1
                        parent[i] = coin
        
        # Reconstruct and update inventory
        result = []
        curr = amount
        while curr > 0 and curr in parent:
            coin = parent[curr]
            result.append(coin)
            self.inventory[coin] -= 1
            curr -= coin
        
        return result if curr == 0 else None

27. Advanced Optimization: Parallel Coin Change

For massive batch processing (e.g., processing millions of transactions):

GPU Acceleration:

import cupy as cp

def coin_change_gpu(amounts, coins):
    """
    Process multiple amounts in parallel on GPU
    """
    max_amount = cp.max(amounts)
    n_amounts = len(amounts)
    
    # Allocate GPU memory
    dp = cp.full((n_amounts, max_amount + 1), cp.inf, dtype=cp.float32)
    dp[:, 0] = 0
    
    # DP on GPU
    for coin in coins:
        for i in range(coin, max_amount + 1):
            dp[:, i] = cp.minimum(dp[:, i], dp[:, i - coin] + 1)
    
    # Extract results
    results = cp.array([dp[idx, amt] for idx, amt in enumerate(amounts)])
    return cp.asnumpy(results)

Distributed Processing (Spark):

from pyspark import SparkContext

def process_batch(amounts, coins):
    sc = SparkContext()
    
    def solve_single(amount):
        dp = [float('inf')] * (amount + 1)
        dp[0] = 0
        for i in range(1, amount + 1):
            for coin in coins:
                if i >= coin:
                    dp[i] = min(dp[i], dp[i - coin] + 1)
        return dp[amount]
    
    amounts_rdd = sc.parallelize(amounts)
    results = amounts_rdd.map(solve_single).collect()
    return results

28. Memory Optimization Techniques

1. Sparse DP (for large amounts):

def coin_change_sparse(coins, amount):
    # Only store reachable states
    dp = {0: 0}
    
    for i in range(1, amount + 1):
        candidates = []
        for coin in coins:
            if i - coin in dp:
                candidates.append(dp[i - coin] + 1)
        
        if candidates:
            dp[i] = min(candidates)
    
    return dp.get(amount, -1)

2. Sliding Window (for streaming amounts):

def coin_change_streaming(coins, max_window=1000):
    """
    Process amounts in a stream without storing full DP table
    """
    dp = [float('inf')] * max_window
    dp[0] = 0
    
    for i in range(1, max_window):
        for coin in coins:
            if i >= coin:
                dp[i] = min(dp[i], dp[i - coin] + 1)
    
    def query(amount):
        if amount < max_window:
            return dp[amount]
        else:
            # Compute on-demand for large amounts
            return coin_change_large(coins, amount)
    
    return query

29. Ethical Considerations

1. Cashless Society:

Optimizing coin change is less relevant as we move to digital payments.
Impact: Marginalizes unbanked populations who rely on cash.
Policy: Laws requiring businesses to accept cash (e.g., in NYC).

2. Algorithmic Pricing:

Dynamic pricing (Uber surge) is a form of resource allocation.
Risk: Price gouging during emergencies.
Regulation: Caps on surge pricing during disasters.

30. Further Reading

“The Art of Computer Programming, Vol 3” (Knuth): Generating functions.
“Algorithms” (Dasgupta): DP chapter.
“Coin Problem” (MathWorld): Frobenius numbers.
“High-Frequency Trading” (Aldridge): Arbitrage strategies.

31. Conclusion

The Coin Change problem is a masterclass in Dynamic Programming. It teaches us about state transition, the importance of loop order (permutations vs. combinations), and the dangers of greedy algorithms. From making change at a bodega to detecting arbitrage opportunities in global FX markets, the principles of “optimizing a sum of parts” are universal. Whether you solve it with a simple 1D array or a complex generating function, mastering Coin Change is a rite of passage for every computer scientist.

32. Summary

Approach	Time	Space	Notes
DP	$O(N \cdot A)$	$O(A)$	Standard solution
BFS	$O(N \cdot A)$	$O(A)$	Graph perspective
Greedy	$O(N)$	$O(1)$	Only for canonical systems

Where $N$ = number of coin types, $A$ = amount.

Originally published at: arunbaby.com/dsa/0038-coin-change

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