Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 59 tok/s
Gemini 2.5 Pro 49 tok/s Pro
GPT-5 Medium 32 tok/s Pro
GPT-5 High 33 tok/s Pro
GPT-4o 127 tok/s Pro
Kimi K2 189 tok/s Pro
GPT OSS 120B 421 tok/s Pro
Claude Sonnet 4.5 36 tok/s Pro
2000 character limit reached

Interpolation of scattered data in $\mathbb{R}^3$ using minimum $L_p$-norm networks, $1<p<\infty$ (1902.07264v1)

Published 19 Feb 2019 in cs.NA

Abstract: We consider the extremal problem of interpolation of scattered data in $\mathbb{R}3$ by smooth curve networks with minimal $L_p$-norm of the second derivative for $1<p<\infty$. The problem for $p=2$ was set and solved by Nielson (1983). Andersson et al. (1995) gave a new proof of Nielson's result by using a different approach. Partial results for the problem for $1<p<\infty$ were announced without proof in (Vlachkova (1992)). Here we present a complete characterization of the solution for $1<p<\infty$. Numerical experiments are visualized and presented to illustrate and support our results.

Citations (2)

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Authors (1)

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube