We investigate a novel approach to approximate tensor-network contraction via the exact, matrix-free decomposition of full tensor-networks. We study this method as a means to eliminate the propagat ...

We investigate the efficient combination of the canonical polyadic decomposition (CPD) and tensor hyper-contraction (THC) approaches. We first present a novel low-cost CPD solver that leverages a ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of computing a matrix inverse using the Newton iteration algorithm. Compared to other algorithms, Newton ...

According to Google DeepMind, AlphaEvolve has successfully discovered multiple new algorithms for matrix multiplication, surpassing the previous AlphaTensor model in efficiency and performance (source ...

Matrix multiplication involves the multiplication of two matrices to produce a third matrix – the matrix product. This allows for the efficient processing of multiple data points or operations ...

Discover how nvmath-python leverages NVIDIA CUDA-X math libraries for high-performance matrix operations, optimizing deep learning tasks with epilog fusion, as detailed by Szymon Karpiński.

Researchers upend AI status quo by eliminating matrix multiplication in LLMs Running AI models without floating point matrix math could mean far less power consumption.

Presenting an algorithm that solves linear systems with sparse coefficient matrices asymptotically faster than matrix multiplication for any ω > 2. Our algorithm can be viewed as an efficient, ...