approximation-ratio-calculator

Analyze and prove approximation ratios for optimization algorithms

509 stars

Best use case

approximation-ratio-calculator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Analyze and prove approximation ratios for optimization algorithms

Teams using approximation-ratio-calculator should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/approximation-ratio-calculator/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/computer-science/skills/approximation-ratio-calculator/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/approximation-ratio-calculator/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How approximation-ratio-calculator Compares

Feature / Agentapproximation-ratio-calculatorStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Analyze and prove approximation ratios for optimization algorithms

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Approximation Ratio Calculator

## Purpose

Provides expert guidance on analyzing approximation algorithms and proving approximation guarantees.

## Capabilities

- LP relaxation analysis
- Integrality gap computation
- Randomized rounding analysis
- Approximation factor derivation
- PTAS/FPTAS feasibility assessment
- Inapproximability results analysis

## Usage Guidelines

1. **Problem Setup**: Formalize the optimization problem
2. **LP Relaxation**: Construct and analyze LP relaxation
3. **Rounding Design**: Design rounding scheme
4. **Ratio Proof**: Prove approximation ratio
5. **Gap Analysis**: Analyze integrality gap

## Tools/Libraries

- LP/ILP solvers
- Symbolic computation
- Proof assistants

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