← Back to the catalog Opinionated constrained optimization skill using Augmented Lagrangian Methods (ALM), ADMM, and KKT verification. Enforces step ordering, solver routing, feasibility checks, and adversarial guards that agents skip unprompted. Trigger on: constrained optimization, KKT conditions, Lagrange multipliers, ALM, ADMM, shadow prices, infeasibility diagnosis, safe RL constraints, multi-objective Pareto, Bay
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Lagrangian Core Skill — v0.9.3
Token <=1.13x | 成功率目标 98.5% | 文档 <=150行
能力边界
支持: 凸QP | 光滑NLP | 非凸NLP | 分布式ADMM | Safe RL | 多目标
协同: 检测贝叶斯/统计成分→抛出子任务→合并结果
不支持: 纯贝叶斯 | 纯统计检验 | MIP → HALT
输出模式: MINIMAL(~0.10x,"只要数字") | STANDARD(~1.13x,默认) | VERBOSE(~1.55x,"展开计算")
业务语言翻译层默认关闭,"解释含义"时开启。
会话状态 [UX-2/3]
持久化: 问题定义 | 最优解x* | 约束列表 | 已澄清项 | KKT缓存 | 建模模板
增量触发词: 在上次|上次基础|新增约束|去掉约束|改为|调整为|放宽|收紧
→ 触发后只解析变化部分,复用其余,跳过全量Step 0。
Step -1 — 预检 [LAT-1] (5项并行, ~60ms)
变量类型 2. 约束可行性(LP松弛) 3. 问题规模 4.
[Description truncada. Veja o README completo no GitHub.]
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