---
title: "convex function"
type: concept
related: [Operations That Preserve Fuction Convexity, Norm, Log Determinant, Inner Product, Singular Value Decomposition]
source: https://www.jemoka.com/posts/kbhconvex_functions/
confidence: high
status: active
---

a function for which, given any two points, the function between those points sits at (lines are convex!) or below the plane given those points
constituents For \(f: \mathbb{R}^{n} \to \mathbb{R}\)
requirements \begin{equation} f\qty(\theta x + \qty(1-\theta) y) \leq \theta f\qty(x) + \qty(1-\theta) f\qty(y) \end{equation}
strictly convex is the strict inequality
additional information log conditions \(f\) is log-linear IFF log \(f\) is affine \(f\) is log-concave iff log \(f\) is concave \(f\) is log-convex IFF log \(f\) is convex check if something is a convex function check definition restrict it to a line: convexity preserve line restriction 1st order condition 2nd order condition show that \(f\) is constructed by operations that preserve fuction convexity some convex functions affine: \(ax + b\) exponential: \(e^{ax}\) powers on \(R_{++}\): \(x^{\alpha}\) for \(\alpha \geq 1\) or \(\alpha \leq 0\) \(|x|^{p}\) for \(p \geq 1\) relu any norm sum of squares: \(\qty {x}^{2}_{2} = x_1^{2} + &hellip; + x_{n}^{2}\) max function: \(\max \qty(x) = \max \qty {x_1 \dots x_{n}}\) softmax: \(\log \qty(\exp x_1 + \dots + \exp x_{n})\) general affine function: \(f\qty(X) = tr\qty(A^{T} X) + b\) (&ldquo;an inner product&rdquo;) spectral norm: \(f\qty(X) = \norm{X}_{2} = \sigma_{\max}\qty(X)\) (the maximum singular value of \(X\) logsumexp: \(f\qty(x) = \log \sum_{k=1}^{n} \exp x_{k}\) quadratic over linear: \(f\qty(x,y) = \frac{x^{2}}{y}, y &gt;0\) quadratic: \(f\qty(x) = \frac{1}{2} x^{T} P x + q^{T} x + r\), with \(P \succeq 0\) is convex least squares: \(f\qty(x) = \norm{Ax - b}^{2}_{2}\) is convex for any \(A\) inverse product: \(f\qty(x) = \frac{1}{\prod_{i=1}^{n} x_{i}}\) inv_pos on \(R_{++}\): \(f\qty(x) = \frac{1}{x}\) is convex if \(x\) is concave and positive some concave fuctions affine square root fractional powers min logs: \(\log x\) entropy: \(- x \log x\) negative part (opposite relu) log determinant: \(f\qty(X) = \log \text{det} X\) geometric mean: \(f\qty(x) = \qty(\prod_{k=1}^{n} x_{k})^{\frac{1}{n}}\) an \(\mathbb{R}_{++}^{n}\) sublevel set \begin{equation} C_{\alpha} = \qty {x \in \text{dom f} \mid f\qty(x) \leq \alpha } \end{equation}
sublevel sets of convex functions are convex sets (but converse is false)
epigraph \begin{equation} \text{epi } f = \qty {\qty(x, t) \in \mathbb{R}^{n+1} \mid x \in \text{dom } f, f\qty(x) \leq t} \end{equation}
f is convex IFF epi f is a convex set
&ldquo;shaded area above the graph&rdquo;
