learn: solve Coin Change (#322) via guided TDD#237
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…initialization bug)
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💡 Educational Note — DP Array Initialization The initialization pattern here is crucial for correctness: const dp = Array.from({ length: amount + 1 }, () => Number.MAX_SAFE_INTEGER);
dp[0] = 0;Why
Complexity: O(amount) space for the DP array. |
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Claude finished @pertrai1's task in 1m 40s —— View job PR Review - Coin Change Solution ✅Great work on solving this classic DP problem through guided TDD! The solution is correct, well-tested, and demonstrates strong understanding of the unbounded knapsack pattern. The POST_MORTEM documentation is exceptional and provides valuable learning insights. 1. Correctness ✅Algorithmic Soundness: The solution is correct and handles all edge cases properly:
Mental Trace Verification: Test Coverage: The 4 test cases strategically cover:
2. Complexity Analysis ✅Time Complexity: O(amount × coins.length) — Accurate The nested loops create Why this matters:
Space Complexity: O(amount) — Accurate Only the Excellent self-review questions in the PR description! The explanations for 3. Learning Focus: Dynamic Programming PatternPattern: Unbounded Knapsack (DP) — Classic variant where items (coins) can be used unlimited times. Why DP is suitable here:
The Key Insight (coin-change.ts:12): dp[i] = Math.min(dp[i], dp[i - coin] + 1);Why this works:
Why
4. Code Quality ✅Strengths:
Minor Observations:
Test Quality:
5. Related Problems for PracticeSince you've mastered the unbounded knapsack pattern, try these variations:
6. Alternative ApproachesCurrent Solution: Bottom-Up DP (Tabulation) ✅
Alternative 1: Top-Down DP (Memoization)
Alternative 2: BFS (Breadth-First Search)
Why your approach is optimal: Bottom-up DP is the standard solution — efficient, clear, and avoids recursion overhead. 7. Common Mistakes & Debugging JourneyThe POST_MORTEM documents an excellent learning moment around DP initialization: The Bug: Initially used Why it matters: This is a classic DP initialization mistake that reveals understanding of how Common Pitfalls with this pattern:
Learning SummaryKey Concepts Demonstrated:
What Makes This Solution Work: Excellent Documentation: Final Verdict: Approve ✅This is a textbook implementation of the unbounded knapsack pattern. The solution is correct, efficient, well-tested, and thoroughly documented. The guided TDD approach successfully built understanding incrementally, and the debugging journey around initialization is a valuable learning artifact. Strengths:
Growth Opportunities (from POST_MORTEM):
Great work on this solution! The TDD journey and documentation make this a valuable learning reference for future DP problems. 🎉 |
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💡 Educational Note — The Unbounded Knapsack Recurrence The heart of this solution is the DP recurrence: for (let i = 1; i <= amount; i++) {
for (const coin of coins) {
if (i - coin >= 0) {
dp[i] = Math.min(dp[i], dp[i - coin] + 1);
}
}
}Breaking it down:
Why it's "unbounded": We can use each coin multiple times (unlike 0/1 knapsack). Complexity: O(amount × coins.length) — the outer loop runs |
✅ Test ResultsTests have been executed for this pull request. 📊 Coverage Summary
Check the workflow logs for detailed test output. |
📝 WalkthroughWalkthroughThis PR adds a complete solution for LeetCode problem "322 - Coin Change," including a TypeScript implementation using bottom-up dynamic programming, comprehensive unit tests, problem documentation, and an interview debrief. It also updates the problems index to reference the new entry. Changes
Estimated code review effort🎯 2 (Simple) | ⏱️ ~12 minutes Poem
🚥 Pre-merge checks | ✅ 3 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (3 passed)
✏️ Tip: You can configure your own custom pre-merge checks in the settings. ✨ Finishing touches
🧪 Generate unit tests (beta)
No actionable comments were generated in the recent review. 🎉 🧹 Recent nitpick comments
Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out. Comment |
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🤖 Gemini Code Review (Model: Here's a detailed review of the 1. Pattern IdentificationThis solution uses Dynamic Programming (DP), specifically a variation of the Unbounded Knapsack problem. The core idea is to build up the solution for larger amounts by leveraging the already computed optimal solutions for smaller amounts. 2. Complexity Verification
3. Key InsightThe key insight for solving the Coin Change problem with Dynamic Programming is recognizing that the minimum number of coins needed to make a target More formally, the "aha!" moment is the recurrence relation: Here:
This bottom-up approach ensures that when we calculate 4. Edge Case AnalysisThe current solution handles several important edge cases:
Potential edge case not explicitly tested (but handled):
5. Learning Points
6. Code QualityThe code quality is excellent:
No significant improvements are needed for code quality; it's already very good. 7. Alternative Approaches
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🤖 Gemini Code Review (Model: Here's a review of the provided LeetCode solution for the "Coin Change" problem: 1. Pattern IdentificationThis solution uses the Dynamic Programming (DP) pattern. Specifically, it's a classic example of the Unbounded Knapsack problem variant, where we want to find the minimum number of items (coins) to reach a target sum (amount), with an unlimited supply of each item. It builds up the solution from smaller subproblems to solve the larger problem. 2. Complexity Verification
3. Key InsightThe key insight for this problem is that the minimum number of coins required to make a certain
This bottom-up approach ensures that when we calculate 4. Edge Case AnalysisThe current solution and its tests handle several important edge cases:
The solution is robust for these common edge cases. 5. Learning Points
6. Code QualityThe code quality is high:
No significant improvements are needed for code quality. 7. Alternative Approaches
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🎯 How This Solution WorksThink of this problem like making change at a store, but you want to give the fewest coins possible. The DP approach builds up the answer by solving smaller subproblems first. The Big IdeaImagine you need to make \ with coins of , , and . Instead of trying every possible combination (exhausting!), we ask:
Answer: Yes! To make , we can:
We try all three options and pick the one with the fewest total coins. The Code JourneyStep 1: Set up the table const dp = Array.from({ length: amount + 1 }, () => Number.MAX_SAFE_INTEGER);
dp[0] = 0;
Step 2: Fill the table bottom-up for (let i = 1; i <= amount; i++) {
for (const coin of coins) {
if (i - coin >= 0) {
dp[i] = Math.min(dp[i], dp[i - coin] + 1);
}
}
}
Step 3: Check the answer return dp[amount] === Number.MAX_SAFE_INTEGER ? -1 : dp[amount];
Why This WorksThis is called Dynamic Programming because we're solving each subproblem once and reusing the results. Instead of recalculating "how do I make ?" every time, we look it up in our table! Time complexity: O(amount × coins.length) — We fill in Space complexity: O(amount) — Just the DP table. The GotchaThe trickiest part? Initialization! We use Related problems: Coin Change II (518), Combination Sum IV (377), Perfect Squares (279) |
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Pull request overview
Adds a new LeetCode medium solution folder for #322 Coin Change to the learning-oriented challenge repository, including implementation, tests, and supporting documentation.
Changes:
- Added a TypeScript dynamic programming solution for Coin Change (unbounded knapsack variant).
- Added a Vitest test suite covering base, impossible, and optimal-solution scenarios.
- Added problem documentation (README + interview-style post-mortem) and updated the central problem index.
Reviewed changes
Copilot reviewed 5 out of 5 changed files in this pull request and generated no comments.
Show a summary per file
| File | Description |
|---|---|
| leetcode/medium/0322-coin-change/coin-change.ts | Implements bottom-up DP to compute the minimum coins needed per amount. |
| leetcode/medium/0322-coin-change/coin-change.test.ts | Adds Vitest coverage for key reachable/unreachable and base-case scenarios. |
| leetcode/medium/0322-coin-change/README.md | Documents the problem statement, examples, and constraints. |
| leetcode/medium/0322-coin-change/POST_MORTEM.md | Adds a guided-TDD interview debrief with cycle-by-cycle notes and analysis. |
| docs/PROBLEMS.md | Registers the new problem in the main index (difficulty + pattern sections). |
📅 Spaced Repetition Reviews Scheduled!Great job solving #0322 - Coin Change! 🎉 To help you retain this knowledge long-term, I've scheduled 5 review sessions using spaced repetition:
What to expect:
🧠 Why Spaced Repetition?Research shows that reviewing material at increasing intervals dramatically improves retention. This adaptive scheduling ensures optimal spacing based on when you actually complete each review! Check |
Problem: #0322 - Coin Change PR: #237 First review scheduled (subsequent reviews auto-scheduled on completion) [skip ci]
Problem
322. Coin Change — Medium
Approach
Dynamic Programming (unbounded knapsack variant). Build up solutions for amounts 0 to target by trying each coin and taking the minimum across all options.
Complexity
Pattern
Dynamic Programming (Unbounded Knapsack)
Learning Summary
dp[i] = min(dp[i], dp[i - coin] + 1)builds optimal solutions by trying all coins for each amount🧠 Self-Review Questions
MAX_SAFE_INTEGERinstead ofMIN_SAFE_INTEGER?Answer
We use `MAX_SAFE_INTEGER` (or `amount + 1`) because we need a sentinel value **larger** than any possible answer. The recurrence uses `Math.min()` to find the smallest coin count. If we initialize with a very negative number like `MIN_SAFE_INTEGER`, it will always be chosen as the minimum, breaking the logic. We want invalid states to start "impossibly large" so any valid coin combination will be smaller and get selected.Answer
The outer loop iterates `amount` times, and the inner loop iterates `coins.length` times. These are **different variables**, so the complexity is O(n × m), not O(n²). If both loops iterated over `amount`, then it would be O(amount²). For this problem with constraints `amount ≤ 10^4` and `coins.length ≤ 12`, the worst case is ~120,000 operations, much better than 100,000,000 if it were O(amount²).Files
README.md— Problem statementcoin-change.ts— Solutioncoin-change.test.ts— Test suite (4 tests)POST_MORTEM.md— Interview debrief with scoring and analysisSummary by CodeRabbit
New Features
Documentation
Tests