ExchangeDEX+

Buy Crypto Markets Spot FuturesGOLD Earn Events

IA2 uses Deep Reinforcement Learning to slash database runtimes by 40%, while new Hyperbolic SVM techniques utilize semidefinite relaxationIA2 uses Deep Reinforcement Learning to slash database runtimes by 40%, while new Hyperbolic SVM techniques utilize semidefinite relaxation

AI-Driven Database Tuning: Faster Index Selection with IA2 and TD3-TD-SWAR

Author: Hackernoon

Source: Hackernoon

2026/01/13 10:12

AI$0.03694-2.71%

FASTER$0.0000596-0.50%

INDEX$0.5013-2.69%

DEEP$0.039771-2.51%

SVM$0.00017731-10.77%

Table of Links

Abstract and 1. Introduction

Related Works
Convex Relaxation Techniques for Hyperbolic SVMs

3.1 Preliminaries

3.2 Original Formulation of the HSVM

3.3 Semidefinite Formulation

3.4 Moment-Sum-of-Squares Relaxation
Experiments

4.1 Synthetic Dataset

4.2 Real Dataset
Discussions, Acknowledgements, and References

\

A. Proofs

B. Solution Extraction in Relaxed Formulation

C. On Moment Sum-of-Squares Relaxation Hierarchy

D. Platt Scaling [31]

E. Detailed Experimental Results

F. Robust Hyperbolic Support Vector Machine

3 Convex Relaxation Techniques for Hyperbolic SVMs

In this section, we first introduce fundamentals on hyperbolic spaces and the original formulation of the hyperbolic Support Vector Machine (HSVM) due to Cho et al. [4]. Next, we present two relaxations techniques, the semidefinite relaxation and the moment-sum-of-squares relaxation, that can be solved efficiently with convergence guarantees. Our discussions center on the Lorentz manifold as the choice of hyperbolic space, since it has been shown in Mishne et al. [6] that the Lorentz formulation offers greater numerical advantages in optimization.

3.1 Preliminaries

:::info Authors:

(1) Sheng Yang, John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA ([email protected]);

(2) Peihan Liu, John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA ([email protected]);

(3) Cengiz Pehlevan, John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, Center for Brain Science, Harvard University, Cambridge, MA, and Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA ([email protected]).

:::

:::info This paper is available on arxiv under CC by-SA 4.0 Deed (Attribution-Sharealike 4.0 International) license.

:::

Market Opportunity

null Price(null)

----

USD

null (null) Live Price Chart

Disclaimer: The articles reposted on this site are sourced from public platforms and are provided for informational purposes only. They do not necessarily reflect the views of MEXC. All rights remain with the original authors. If you believe any content infringes on third-party rights, please contact [email protected] for removal. MEXC makes no guarantees regarding the accuracy, completeness, or timeliness of the content and is not responsible for any actions taken based on the information provided. The content does not constitute financial, legal, or other professional advice, nor should it be considered a recommendation or endorsement by MEXC.