Differentiable Optimization API
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評估
- 難度
- 5/5
- 預估耗時
- 一週以上
- 新手友好度
- 25/100
- Issue 類型
- 功能
- 描述清晰度
- 需要釐清
- 活躍度
- 停滯
- 技術堆疊
- julia
研究方向
從 DiffOpt.jl PR #344 中的初始討論開始，並比較其中引用的 DiffOpt lpbasis、MadDiff 和 Moreau 實作。檢查它們如何處理敏感度屬性、微分函式、重設、參數、係數函式、惰性 getter 以及 JuMP 層級的輔助函式；由於沒有指定目標檔案或測試，因此完成的標準是達成一個一致的最小 API。
由索引模型根據 Issue 內容生成。
描述
What is the minimal API for differentiable optimization?
- Attributes to pass input forward and reverse sensitivities.
- forward inputs sensitivities: are numbers associated with the coefficients of the problem or parameters
- reverse inputs sensitivities: are numbers associated with variables
- functions to perform the diff (analogous to optimize!)
- Attributes to query output forward and reverse sensitivities.
- forward outputs sensitivities: are numbers associated with variables
- reverse outputs sensitivities: are numbers associated with the coefficients of the problem or parameters
Important aspects:
a. A function to reset all input sensitivities is extremely important in practice, as resetting manually is very inefficient.
b. Parameters are extremely useful here. Having used DiffOpt and talking to users it is clear that it is very error-prone to touch coefficients directly (also see: https://arxiv.org/abs/2510.25986). POI is almost essential here.
c. Passing coefficients individually is suboptimal because it does not play well with bridges. Passing coefficient sensitivities as functions is key to making it work smoothly. For getters, Lazy operations are importants as we are usually no interested in absolutely every single coefficient
d. In practice, helper methods for objective sensitivity are a lifesaver.
e. Having a JuMP level API is much more ergonomic than having MOI.set and get all around.
We already have use case for existing solvers:
- https://github.com/jump-dev/DiffOpt.jl/tree/jg/lpbasis (HiGHS, Gurobi, Xpress)
- MadNLP (https://github.com/MadNLP/MadDiff.jl/tree/main/ext/MathOptInterfaceExt)
There is a new solver that proposes to do it as well: - Moreau (https://github.com/jump-dev/DiffOpt.jl/pull/344)
Additional points:
- In DiffOpt, we did not put much effort into sensitivities associated with duals. Attribute-wise, there is nothing special here, but we never have a concrete use case, so we did not venture into it.
Initial discussion in: https://github.com/jump-dev/DiffOpt.jl/pull/344
- 主要語言
- Julia
- 星號
- 506
- 分支
- 103
- 平均合併
- 1 天 10 分鐘
- 30 天內合併 PR
- 12
環境準備
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從這裡開始
- 先讀完整個 Issue，再讀專案的貢獻指南。
- 在 Issue 下留言說明你要接手 —— 這能避免兩個人做同樣的事。
- Fork 儲存庫，在一個分支上完成修改。
- 送出 Pull Request，並在描述裡引用這個 Issue 編號。
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