Run1 pareto frontier optimization
Identifying Pareto-optimal points in a multi-objective optimization space.From its SKILL.md
npx -y skills add cxcscmu/SkillLearnBench --skill run1_pareto-frontier-optimizationAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
SKILL.md
0.7 KB, 168 tokens by cl100k_base, as published. Nobody here has run it
Pareto Logic
A solution $(F1_a, \Delta_a)$ dominates $(F1_b, \Delta_b)$ if:
- $(F1_a \ge F1_b)$ and $(\Delta_a \le \Delta_b)$
- At least one inequality is strict.
To extract the Pareto frontier from a results list:
- Initialize an empty list
pareto_points. - For each candidate $A$:
- Check if any other candidate $B$ exists such that $B$ dominates $A$.
- If no $B$ dominates $A$, $A$ is on the frontier.
Efficiency Tip: For large search spaces, sort by $F1$ descending, then iterate through to keep only those that show a decrease in $\Delta$.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.