Suggest edit — operations that preserve fuction convexity

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title: "operations that preserve fuction convexity"
source: https://www.jemoka.com/posts/kbhoperations_that_preserve_fuction_convexity/
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non-negative scaling sum: \(f_{1}+ f_{2}\) is convex if \(f_1, f_2\) is convex infinite sums: \(\sum_{i=1}^{\infty} f_{i}\) is convex integral: if \(f\qty(x,a)\) is convex in \(x\), \(\int_{a \in A} f\qty(x,a) \dd{a}\) is convex pre-composition with affine function: \(f\qty(Ax + b)\) is convex if \(f\) is convex pointwise maximum: \(f_{1}, &hellip;, f_{m}\) is convex, then \(f\qty(x) = \max \qty(f_{1} \qty(x)\dots f_n \qty(x))\) is convex supremum: if \(f\qty(x,y)\) is convex in \(x\) far each \(\text{sup}_{y \in Y} f\qty(x,y)\) partial minimization: \(f\qty(x) = \text{inf}_{y \in C} f\qty(x,y)\) (find the smallest value of \(f\) over \(y \in C\), or the point at which its approached) perspective of convex function is convex \(\text{persp}\qty(a,b) = b f\qty(\frac{a}{b})\) conjugate function of any function is convex composition with scalar functions \(g : \mathbb{R}^{n} \to \mathbb{R}\), \(h: \mathbb{R} \to \mathbb{R}\), and let \(f = h\qty(g\qty(x)) = h \odot g\)
composition \(f\) is convex if:
\(g\) convex, \(h\) convex, extended-value extension \(\tilde{h}\) non decreasing \(g\) concave, \(h\) convex, extended-value extension \(\tilde{h}\) non increasing composition \(f\) is concave if:
\(g\) concave, \(h\) concave, extended-value extension \(\tilde{h}\) non decreasing \(g\) convex, \(h\) concave, extended-value extension \(\tilde{h}\) non increasing general composition rule that preserve convexity composition of \(g : \mathbb{R}^{n} \to \mathbb{R}^{k}\), and \(h: \mathbb{R}^{k} \to \mathbb{R}\) is \(f\qty(x) = h\qty(g\qty(x)) = h\qty(g_{1}\qty(x), \dots, g_{k}\qty(x))\)
\(f\) is convex if \(h\) is convex and for each \(i\), one of the the following holds:
\(g_{i}\) convex, \(\tilde h\) nondecreasing in its \(i\) th element \(g_{i}\) concave, \(\tilde h\) nonincreasing in its \(i\) th element \(g_{i}\) affine examples sum of the \(r\) largest elements of a set is convex since we can multiply them with many-hot selectors which gives you combinations an and then max them together