There are few things more pleasing to your average mathematician than when a result surprises you. Take e, for example – a transcendentally irrational number equal to a little more than 2.7 – and ...

High-performance matrix multiplication remains a cornerstone of numerical computing, underpinning a wide array of applications from scientific simulations to machine learning. Researchers continually ...

Abstract: In this paper, we propose three modular multiplication algorithms that use only the IEEE 754 binary floating-point operations. Several previous studies have used floating-point operations to ...

When you create an algorithm, you need to include precise, step-by-step instructions. This means you will need to break down the task or problem into smaller steps. We call this process decomposition.

As Transformer models continue to grow in size and complexity, numerous high-fidelity pruning methods have been proposed to mitigate the increasing parameter count. However, transforming these ...

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Abstract: General sparse matrix-matrix multiplication (SpGEMM) is a fundamental computational method with wide-ranging applications in scientific simulations, machine learning, and image processing.

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The Recent changes chapter of the reference manual explains the differences and the incompatibilities of each new version of Coq. If you upgrade Coq, please read it carefully as it contains important ...