characterizing-running-times
CommunityMaster asymptotic analysis for algorithm running times.
Software Engineering#asymptotic#algorithm-analysis#big-o#recurrences#notational-analysis#time-complexity
AuthorArcadi4
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This skill helps developers and students determine and justify the simplest precise growth bound for an algorithm's running time using asymptotic notation, ensuring clarity about whether bounds apply to worst-case, best-case, or all inputs.
Core Features & Use Cases
- Determine O, Omega, Theta, little-o, and little-omega bounds for loops and recurrences.
- Apply CLRS conventions for mathematical formatting, proofs, and structured answers.
- Validate claims about running time and space across different input scenarios and growth orders.
Quick Start
Analyze a given algorithm to determine its tight asymptotic running time bound and provide a justification using O, Omega, Theta as appropriate.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: characterizing-running-times Download link: https://github.com/Arcadi4/nerdy/archive/main.zip#characterizing-running-times Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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