机器学习-算法理论

Constrained Optimization - Solvi

2021-05-26  本文已影响0人  shudaxu

Intuition of KKT

finding supporting hyperplane[1]

设定

Lagrange multiplier本身适用于等式constraints,KKT multiplier适用于非等式,是Lagrange的一个泛化。
\min_{ \vec x \in R^n} f(\vec x)
s.t.\{ g_i(\vec x) \leq 0,h_j(\vec x) = 0\}

KKT condition

可见之前的讨论[4]

KKT Qualification

即在什么情况下,原问题的极值点\vec x^*需要满足KKT condition
常用:
LCQ:等式与不等式约束都为仿射affine变换。
LICQ:对于激活[2] 的不等式约束,与等式约束,在极值处的gradient是线性无关的。(在向量空间中,对于一组向量,存在任意非全0的线性组合使其和为0,则为线性相关)
SC:Slater's condition,f,g_i是convex,h_i都是仿射变换。

Solving KKT System

Refer
[1] https://en.wikipedia.org/wiki/Supporting_hyperplane
相关理论,https://en.wikipedia.org/wiki/Hyperplane_separation_theorem

[2]:只有当c_i(x)=0,即x在边界处时,才是激活的。即其项对应的kkt乘子\lambda_i > 0

[3]求解思路:
Doc:A Karush-Kuhn-Tucker Example
The system of equations and inequalities corresponding to the KKT conditions is usually not solved directly, except in the few special cases where a [closed-form] solution can be derived analytically. In general, many optimization algorithms can be interpreted as methods for numerically solving the KKT system of equations and inequalities

[4]KKT conditions:
https://www.jianshu.com/p/1a733971b25e
KKT condition for convex:
sufficiency?:https://math.stackexchange.com/questions/2325637/sufficiency-of-kkt-conditions-when-the-objective-function-is-convex-on-the-inter
sufficient or necessary?https://math.stackexchange.com/questions/2513300/is-kkt-conditions-necessary-and-sufficient-for-any-convex-problems
Connection between KKT condition & EL equation:
https://math.stackexchange.com/questions/1898887/connection-between-euler-lagrange-equations-and-kkt

[5]求解过程
例:https://www.jianshu.com/writer#/notebooks/31109505/notes/87921110/preview
例&dual:https://math.stackexchange.com/questions/2829235/kkt-optimality-conditions-in-optimization-exercise
例:https://math.stackexchange.com/questions/3197212/solve-kkt-conditions-of-the-following-problem,(评论)

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