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# System prepended metadata

title: Recourse

---

#### Goal
M(x) A black box
Optimizier (SGD、Adam)

Gradient-free (1d array)
[a, b, c] action

#### Cost
Real world scenario

Sum of Local Cost

L1 distance
norm(1) = dist = (x^1 + y^1) ^ (1/1) = |x| + |y|

L2 distance
norm(2) = dist = (x^2 + y^2) ^ (1/2)

norm(n) = (x^n + y^n) ^ (1/n)

Cost (n dim) -> C(n, 2)

相關度 <-> linear regression's M

#### Beam Search

#### reference paper 
- about cost function:
    1. [Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions](https://arxiv.org/abs/2111.01235)
        - core: 除非徹底瞭解user的background，我們很難設計好的cost func.
        - 