Abstract: In applied and numerical algebraic geometry, many problems are reduced to computing an approximation to a real algebraic curve. In order to elevate the results of such a computation to the ...

We propose the Trust Region Preference Approximation (TRPA) algorithm ⚙️, which integrates rule-based optimization with preference-based optimization for LLM reasoning tasks 🤖🧠. As a ...

Abstract: Noncommutative constraint satisfaction problems (CSPs) are higher-dimensional operator extensions of classical CSPs. Their approximability remains largely unexplored. A notable example of a ...

Recent advancements in quantum computing and quantum-inspired algorithms have sparked renewed interest in binary optimization. These hardware and software innovations promise to revolutionize solution ...

In recent years, neural networks have once again triggered an increased interest among researchers in the machine learning community. So-called deep neural networks model functions using a composition ...

\[ \gdef\bias{\mathrm{bias}} \gdef\deg{\mathrm{deg}} \gdef\indeg{\mathrm{indeg}} \gdef\outdeg{\mathrm{outdeg}} \gdef\Snap{\mathrm{Snap}} \gdef\RSnap{\mathrm{RefSnap ...

ABSTRACT: The purpose of reoptimization using approximation methods—application of knowledge about the solution of the initial instance I, provided to achieve a better quality of approximation ...

Algorithms have taken on an almost mythical significance in the modern world. They determine what you see on social media and when browsing online, help form people’s belief systems, and impact the ...