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Choose and run a gradient method v1.0.0
Use this resource to minimize a differentiable function with a gradient method
New
- 408 downloads
- 209 likes
- 217 saves
Free
Install with the CLI
In a terminal, from the folder you want:
npx --yes palatekit@latest install gradient-descent-method-selectionWith no option, the skill goes to ~/.agents/skills/; add --agent claude-code (or codex, cursor…) to target an agent.
First time: connect the CLI with
npx --yes palatekit@latest loginThe agent picks the step rule and the method, for example BFGS or conjugate gradient with SciPy, computes the gradient with SymPy and explains the expected convergence. You get a numerical minimum and a justified choice.
Mostly picked by
Updated November 2, 2026
Previews
Details
- Install guides
- Generates with
- Your agent's model
- Licence
- MIT
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What you get
- SKILL.md: decision tree (descent, step size, convergence)
- SKILL.md: SciPy commands (BFGS, cg) and SymPy (gradient)
Good fit for
- You minimize a differentiable cost function
- You compare descent step rules
Not for
Tags
- optimization
- gradient
- bfgs
- scipy
About
You say
“Minimize f(x,y) = x² + 3y² with a gradient method.”
Skill published by parcadei under the MIT license, delivered in its original version.
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How to use it
- Install the skill in your agent's skills folder
- Ask your agent to minimize a differentiable function with a gradient method
- Review and adjust the result
Content preview
Files
- LICENSE1.1 KB
- NOTICE-PALATEKIT.md0.5 KB
- SKILL.md4 KB
Origin and licence
Listed unmodified
- Author
- parcadei
- Source
- github.com/parcadei/continuous-claude-v3/tree/d07ff4b06b62f43771bc0c927d0211b734d6149e/.claude/skills/math/optimization/gradient-methods
- Licence
- MIT
Notice
« gradient-methods » (parcadei, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.
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