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SCS 编译步骤,基本算法流程和实现。
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Friberg - 2023 - Projection onto the exponential cone a univariate root-finding problem.pdf
closed conic hull of its epigraph (conic extensions)
凸函数的上图可以用锥闭包(凸包)表示,只需增加一个透视 perspective 维度$(x,t\ge f(x))$ → $(x,s,t),s>0$
$$ cl\{(t,s,x)\in \mathbb R^{1\times 1\times n}\mid \frac ts\ge f(\frac xs),s\ge 0\} $$
单纯的凸集不能满足cone $kv\in \Omega,\forall v\in \Omega$

注意,“透视源”不一定是点,可以是线、面,只要作为极限存在并以边界点囊括入闭包即可。
Rockafellar, R.T.: Level sets and continuity of conjugate convex functions. Trans. Amer. Math. Soc. 123(1), 46–63 (1966). https://doi.org/10.1090/S0002-9947-1966-0192318-X
如指数锥常表示为第三象限和透视锥的并集。
指数函数生成指数锥
$$ \mathcal K_{\exp}\equiv cl([\mathcal K_{\exp}]{++}) =[\mathcal K{\exp}]{++} \cup[\mathcal K{\exp}]_{0} $$
其中$[\mathcal K_{\exp}]_{++}$是指数函数符合透视的集合(perspective interior)
$$ [\mathcal K_{\exp}]_{++} =\big\{ (t,s,x)\in \mathbb R^{1\times 1\times n}\mid \frac ts\ge \exp(\frac xs),s\ge 0 \big\} $$
作为闭包,需要考虑并包括边界点。这里就是取$s\to 0$后只有极限定义的点集(指数函数被无穷缩小,取极限后上图为第四象限全体, perspective boundary)
$$ [\mathcal K_{\exp}]_{0} =\big\{ s=0,t\ge 0,r\le 0 \big\} $$
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Friberg - 2023 - Projection onto the exponential cone a univariate root-finding problem.pdf
指数锥优势
expressive abilities
指数函数上图,对数函数下图,exponentials指数多项式,logarithms,entropy funxtions熵,product logarithms, soft-max, soft-plus
numerically stable 3-self-concordant barrier functions
兼容对偶、facial reduction、内点特性
非symmetric cone,仍然能够快速收敛 </aside>
指数锥的投影问题
projection problem
$$ d(\vec v_0)=\min \|\vec v-\vec v_0\|2 \\ s.t. \ \ v\in \mathcal K{\exp} $$
最优性条件
KKT 条件,或者The Moreau decomposition theorem