---
id: wiki-2026-0508-problem-solving
title: Problem Solving
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Problem Solving, 문제 해결, decomposition]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [methodology, meta, decomposition, heuristics]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: meta
  framework: methodology
---

# Problem Solving

## 매 한 줄
> **"매 큰 문제를 매 작은 문제로 매 쪼개고 매 합쳐라"**. Problem Solving은 매 ill-defined situation을 매 well-defined sub-problem 으로 매 decompose 하고 매 solve → compose 하는 매 universal methodology. Polya (1945) 부터 매 modern algorithmic thinking, 매 LLM tool-use planning 까지 매 backbone.

## 매 핵심

### 매 4-step (Polya)
1. **Understand**: input/output/constraint 매 명확화.
2. **Plan**: 매 known problem과 매 mapping, 매 sub-goal 분해.
3. **Execute**: 매 plan 매 step-by-step.
4. **Review**: 매 verify, 매 generalize.

### 매 Heuristic toolkit
- **Decomposition**: divide-and-conquer.
- **Analogy**: 매 known problem → 매 transform.
- **Working backward**: goal에서 매 출발.
- **Invariant**: 매 변하지 않는 property 매 식별.
- **Specialization**: 매 simpler case 먼저.
- **Generalization**: 매 더 일반 case로 매 abstract.

### 매 응용
1. Algorithm design.
2. System architecture (decomposition into services).
3. Debugging (Problem Solving Skills 참고).
4. LLM agent planning (ReAct, ToT).
5. Research project scoping.

## 💻 패턴

### Decomposition template
```python
# 매 1. Restate
# Goal: 매 sort N items by key with stable + in-place
# Inputs: list[T]; Outputs: list[T] sorted
# Constraints: stable, O(1) extra space, T comparable

# 매 2. Plan — sub-problems
# (a) partition pivot          (in-place quicksort)
# (b) but quicksort 매 unstable → swap to merge sort?
# (c) merge sort 매 not in-place → block merge sort (Wikisort)

# 매 3. Execute — pick block merge sort
def block_merge_sort(a): ...  # 매 implement

# 매 4. Review — invariants, edge cases (empty, dupes, all-equal)
```

### Working backward (puzzle solving)
```python
# Find x such that f(g(h(x))) == target
# Backward: y = f^-1(target); z = g^-1(y); x = h^-1(z)
def backward(target, inverses):
    cur = target
    for inv in reversed(inverses):
        cur = inv(cur)
    return cur
```

### Invariant-based proof (loop)
```python
def gcd(a, b):
    # 매 Invariant: gcd(a0, b0) == gcd(a, b) at every iteration
    while b:
        a, b = b, a % b
    return a
```

### Specialization → Generalization
```python
# 매 Step 1 — special case: 매 sorted list, no duplicates
def find_special(arr, t):
    lo, hi = 0, len(arr)-1
    while lo <= hi:
        mid = (lo+hi)//2
        if arr[mid] == t: return mid
        if arr[mid] < t: lo = mid+1
        else: hi = mid-1
    return -1

# 매 Step 2 — generalize: 매 with duplicates → leftmost binary search
def find_general(arr, t):
    lo, hi = 0, len(arr)
    while lo < hi:
        mid = (lo+hi)//2
        if arr[mid] < t: lo = mid+1
        else: hi = mid
    return lo if lo < len(arr) and arr[lo] == t else -1
```

### LLM agent decomposition (ReAct loop)
```python
# 매 Pseudo-ReAct
def solve(task, llm, tools, max_steps=10):
    history = [{"role": "user", "content": task}]
    for _ in range(max_steps):
        out = llm.chat(history)              # Thought + Action
        if out.is_final: return out.answer
        result = tools[out.action](out.args)  # Observation
        history += [out.message, {"role": "tool", "content": result}]
    return None
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Algorithm puzzle | Polya + decomposition |
| System design | Component decomposition + interface |
| Debugging | [[Problem Solving Skills]] (repro/bisect) |
| Research | Specialization → generalization |
| LLM agent | ReAct / Tree-of-Thoughts |

**기본값**: Understand → Decompose → Solve smallest → Compose → Review.

## 🔗 Graph
- 변형: [[Polya-Method]]
- 응용: [[System-Architecture]]
- Adjacent: [[Problem Solving Skills]] · [[Heuristic]] · [[ReAct]]

## 🤖 LLM 활용
**언제**: ill-defined task scoping, 매 multi-step planning, 매 agentic workflow 설계.
**언제 X**: 매 1-line trivial task.

## ❌ 안티패턴
- **매 Skip understanding**: 매 problem 매 명확하지 않은 채 매 코딩 시작.
- **매 Premature optimization**: 매 sub-problem 매 미해결인데 매 perf tune.
- **매 No review**: 매 동작하면 매 commit, 매 generalization 안 함.
- **매 Cargo-cult algorithm**: 매 비슷한 문제의 매 solution 매 무비판 복붙.

## 🧪 검증 / 중복
- Verified (Polya "How to Solve It" 1945, Schoenfeld "Mathematical Problem Solving" 1985).
- 신뢰도 A.
- 관련: [[Problem Solving Skills]] (debugging-focused sibling).

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — Polya + heuristic toolkit + algorithmic patterns |
