Algorithms

This section is about building a reusable method library, not just collecting solved problems.

Reading order

1. The real risk in algorithm study is not difficulty, but lack of feedback loops
2. Before solving more problems, build complexity intuition first
3. Problem classification is pattern recognition, not label memorization
4. Algorithm review template: turn “I solved it once” into “I own the method”
5. Why dynamic programming so often feels like “I understand it when I read it, but I still cannot solve it”

Dynamic programming

• Start DP from state definition, not from formulas
• Many DP problems are really about choices
• When to move from memoization to a DP table

Pattern series

• Two pointers: when to think in terms of narrowing from both ends
• Sliding window: when interval problems do not need full recomputation
• Binary search: the real challenge is monotonicity and boundary design
• Monotonic stack: why so many “next greater element” problems collapse into one pattern
• Backtracking is constrained search, not random brute force

Data structure series

• Why linked list problems fall apart the moment pointer updates lose discipline
• Tree problems depend more on recursion semantics than traversal names
• Graph problems become much easier once the model is explicit

Roadmap

• Algorithm study roadmap: from intuition to a method library