Abstract: Linear programming is a central problem in computer science and applied mathematics with numerous applications across a wide range of domains, including machine learning and data science.

Abstract: The von Neumann entropy, named after John von Neumann, is an extension of the classical concept of entropy to the field of quantum mechanics. From a numerical perspective, von Neumann ...

Abstract: Exact computation of linear algebra operations is challenging or even impossible at extreme scale By leveraging randomization we can get approximate results at reduced computational cost ...

ランダム投影は、データの構造を捉えながら次元を下げることができ、今日大量のデータを扱う機械学習、信号処理、情報検索などの基本ツールとなっている。RandNLA (Randomized Numerical Linear Algebra) は、ランダム投影を活用してテンソルの低ランク分解の計算量 ...

The von Neumann entropy, named after John von Neumann, is an extension of the classical concept of entropy to the field of quantum mechanics. From a numerical perspective, von Neumann entropy can be ...

Evaluating several new approaches to improve convergence of Randomized Kaczmarz (RK) for consistent ill-conditioned systems. This project explores the availability of convergence information among ...

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In statistics and machine learning, logistic regression is a widely-used supervised learning technique primarily employed for binary classification tasks. When the number of observations greatly ...