# Efficient Algorithms for Approximate Smooth Selection

## TL;DR

This paper introduces efficient algorithms for approximate smooth selection problems, enabling the construction of smooth interpolating functions with bounded norms from convex set data in near-linear time.

## Contribution

The paper presents novel algorithms that solve approximate smooth selection problems efficiently, with proven complexity bounds and applicability to convex set data.

## Key findings

- Algorithm runs in C(τ) N log N steps
- Constructs smooth interpolants with bounded norms
- Applicable to convex set data in high dimensions

## Abstract

In this paper we provide efficient algorithms for approximate $\mathcal{C}^m(\mathbb{R}^n, \mathbb{R}^D)-$selection. In particular, given a set $E$, constants $M_0 > 0$ and $0 <\tau \leq \tau_{\max}$, and convex sets $K(x) \subset \mathbb{R}^D$ for $x \in E$, we show that an algorithm running in $C(\tau) N \log N$ steps is able to solve the smooth selection problem of selecting a point $y \in (1+\tau)\blacklozenge K(x)$ for $x \in E$ for an appropriate dilation of $K(x)$, $(1+\tau)\blacklozenge K(x)$, and guaranteeing that a function interpolating the points $(x, y)$ will be $\mathcal{C}^m(\mathbb{R}^n, \mathbb{R}^D)$ with norm bounded by $C M_0$.