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Formulate, solve, and verify finite-dimensional optimization and calculus-of-variations problems. Use for convex programs, constrained extrema, KKT systems, duality, optimal control, Euler-Lagrange equations, and numerical optimization.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/eightmm/codex-science --skill optimization-variational-methods命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Execute auditable quantitative research across preregistered study design, statistical inference, numerical verification, dimensional analysis, uncertainty propagation, counterexample search, and formal proof. Use when a scientific or mathematical question needs actual computation and machine-readable evidence rather than only explanation.
Compare finite actions under uncertainty with explicit states, loss, utility or risk-neutral payoff, priors, experiment likelihoods, and a declared decision criterion. Use for Bayes risk, expected utility or payoff, pure-action minimax, minimax regret, dominance, posterior decision policies, EVPI/EVSI, experiment-cost decisions, and prior-threshold sensitivity; do not use for ordinary statistical testing, causal identification, strategic games, or MDP/POMDP/RL planning.
Compare finite actions under uncertainty with explicit states, loss, utility or risk-neutral payoff, priors, experiment likelihoods, and a declared decision criterion. Use for Bayes risk, expected utility or payoff, pure-action minimax, minimax regret, dominance, posterior decision policies, EVPI/EVSI, experiment-cost decisions, and prior-threshold sensitivity; do not use for ordinary statistical testing, causal identification, strategic games, or MDP/POMDP/RL planning.
基于 SOC 职业分类
正在显示 SKILL.md
| name | optimization-variational-methods |
| description | Formulate, solve, and verify finite-dimensional optimization and calculus-of-variations problems. Use for convex programs, constrained extrema, KKT systems, duality, optimal control, Euler-Lagrange equations, and numerical optimization. |
| license | MIT |
Write the objective, variables or admissible functions, domain, equality and inequality constraints, boundary conditions, regularity class, units, and whether the task seeks a local or global optimum. Remove arbitrary scale or gauge freedom. Check feasibility and existence before choosing an algorithm.
Scale variables and residuals, choose tolerances tied to the scientific question, and retain iteration history. For discretized functionals, distinguish discretization error from optimizer termination. Do not infer a global optimum from a single local solver run unless a theorem or valid bound supports it.
Report formulation, existence or convexity conditions, solver and initialization, residuals, bounds, sensitivity, and whether the conclusion is local, global, or heuristic.
Original synthesis informed by open calculus-of-variations and numerical-analysis sources recorded in
../../docs/TEXTBOOK_SOURCES.md.
