Convex Functions
This skill analyzes functions to determine if they are convex, crucial for optimization and understanding stability in various fields.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add convex-functions npx -- -y @trustedskills/convex-functions
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"convex-functions": {
"command": "npx",
"args": [
"-y",
"@trustedskills/convex-functions"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
The convex-functions skill enables AI agents to analyze and work with convex functions, which are mathematical functions where the line segment between any two points on the graph lies above or on the graph. This includes identifying convexity, computing derivatives, and solving optimization problems involving convex functions.
When to use it
- When optimizing a function in machine learning or operations research that is known to be convex.
- When verifying if a given mathematical function exhibits convex properties for theoretical analysis.
- During algorithm development where convex optimization techniques are required, such as in support vector machines or linear programming.
Key capabilities
- Analyze the convexity of functions using second derivatives.
- Solve optimization problems with convex objective functions.
- Compute gradients and Hessians for convex function evaluation.
Example prompts
- "Is the function f(x) = x² + 3x convex?"
- "Find the minimum value of the convex function f(x) = e^x."
- "Verify if the given function is convex and compute its Hessian matrix."
Tips & gotchas
- Ensure that the input functions are differentiable, as many convexity checks rely on derivatives.
- Be aware that not all functions are convex; some may be concave or neither, which can affect optimization results.
Tags
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