Convex
Convex helps you find the optimal solution to a problem by systematically exploring all possible choices and selecting the best one based on defined criteria.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add convex npx -- -y @trustedskills/convex
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"convex": {
"command": "npx",
"args": [
"-y",
"@trustedskills/convex"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
The convex skill provides tools and functionalities related to convex optimization, enabling AI agents to solve mathematical problems involving convex functions and constraints. It supports modeling and solving optimization tasks that are common in fields like economics, engineering, and machine learning.
When to use it
- To optimize resource allocation under linear or quadratic constraints.
- For training models that require minimizing convex loss functions.
- In scenarios where you need to find the global minimum of a function efficiently.
- When solving problems involving portfolio optimization or signal processing.
Key capabilities
- Solving convex optimization problems using efficient algorithms.
- Modeling and handling linear, quadratic, and conic constraints.
- Integration with numerical computing libraries for accurate results.
Example prompts
- "Solve this convex optimization problem: minimize x² + y² subject to x + y = 1."
- "Find the optimal portfolio allocation given convex risk constraints."
- "Train a model using convex loss minimization."
Tips & gotchas
- Ensure that the problem you're solving is indeed convex, as non-convex problems may not yield accurate results.
- Be mindful of numerical precision and scaling when working with large datasets or complex constraints.
Tags
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