ReddRadar
Agent skill
complexity-analysis
Analyze algorithm complexity, understand Big O notation, computability theory, NP-completeness, and computational limits.
Install this agent skill to your Project
npx add-skill https://github.com/pluginagentmarketplace/custom-plugin-computer-science/tree/main/skills/theory
SKILL.md
Complexity Analysis Skill
Skill Metadata
skill_config:
version: "1.0.0"
category: theoretical
prerequisites: [cs-foundations, algorithms]
estimated_time: "4-6 weeks"
difficulty: advanced
parameter_validation:
analysis_type:
type: string
enum: [time, space, both]
default: both
notation:
type: string
enum: [big-o, theta, omega]
retry_config:
max_attempts: 3
backoff_strategy: exponential
initial_delay_ms: 500
observability:
log_level: INFO
metrics: [analysis_accuracy, master_theorem_usage]
Quick Start
Scientifically measure algorithm performance and understand computational limits.
Asymptotic Notation
Big O (Upper Bound)
- f(n) = O(g(n)) if f(n) ≤ c·g(n) eventually
- Describes worst-case behavior
- Example: Insertion sort is O(n²)
Theta (Tight Bound)
- f(n) = θ(g(n)) if c₁·g(n) ≤ f(n) ≤ c₂·g(n)
- Exact growth rate
- Example: Merge sort is θ(n log n)
Omega (Lower Bound)
- f(n) = Ω(g(n)) if f(n) ≥ c·g(n) eventually
- Describes best-case behavior
Common Complexity Classes
O(1) < O(log n) < O(n) < O(n log n) < O(n²) < O(n³) < O(2ⁿ) < O(n!)
Master Theorem
For T(n) = a·T(n/b) + f(n):
Case 1: f(n) < n^log_b(a) → T(n) = O(n^log_b(a))
Case 2: f(n) = n^log_b(a) → T(n) = O(n^log_b(a) · log n)
Case 3: f(n) > n^log_b(a) → T(n) = O(f(n))
Complexity Classes
P (Polynomial): Solvable in polynomial time NP (Nondeterministic Polynomial): Solutions verifiable in polynomial time NP-Complete: Hardest in NP NP-Hard: At least as hard as NP-complete
Troubleshooting
| Issue | Root Cause | Resolution |
|---|---|---|
| Wrong Big-O | Hidden loops ignored | Trace all operations |
| Master Theorem fails | Conditions not met | Use substitution method |
| Empirical mismatch | Hidden constants | Count operations precisely |
Practical Complexity
| Complexity | Size n | Algorithm |
|---|---|---|
| O(log n) | 10⁸ | Binary search |
| O(n) | 10⁷ | Linear scan |
| O(n log n) | 10⁶ | Good sort |
| O(n²) | 10⁴ | Slow sort |
| O(2ⁿ) | 20 | Exponential |
NP-Complete Problems
Famous examples:
- 3-SAT: Boolean satisfiability
- TSP: Traveling salesman
- Knapsack: 0/1 knapsack
- Clique: Find complete subgraph
- Vertex cover: Cover all edges
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
pluginagentmarketplace/custom-plugin-computer-science
cs-foundations
Master discrete mathematics, logic, formal proofs, and computational thinking. Build the mathematical foundation for all computer science.
pluginagentmarketplace/custom-plugin-computer-science
advanced-topics
Explore advanced CS topics including advanced data structures, parallel computing, security, functional programming, and quantum computing.
pluginagentmarketplace/custom-plugin-computer-science
data-structures
Master selection and implementation of data structures. Learn when to use arrays, lists, trees, graphs, heaps, and hash tables for optimal performance.
pluginagentmarketplace/custom-plugin-computer-science
algorithms
Master algorithm design, common patterns, optimization techniques, and problem-solving strategies. Learn to solve any computational challenge efficiently.
pluginagentmarketplace/custom-plugin-computer-science
systems-computing
Understand computer systems from digital logic through operating systems, networks, databases, and distributed systems.
pluginagentmarketplace/custom-plugin-ai-data-scientist
deep-learning
Neural networks, CNNs, RNNs, Transformers with TensorFlow and PyTorch. Use for image classification, NLP, sequence modeling, or complex pattern recognition.
Didn't find tool you were looking for?