However, implementing the algorithm correctly and efficiently still requires a lot of work. (5/5). Read our blog to find out more: To use the library see:

Verifying SVD in a ZK-circuit boils down to verifying matrix multiplication. While computing the product of N X N matrices costs O(N^2.8) operations, verifying a product is much easier and only costs O(N^2) using Freivalds’ algorithm (. (4/5).

SVD is also useful for the decentralized reputation management algorithm EigenTrust. (3/5).

SVD allows one to represent a matrix as a product of two orthogonal matrices (rotations) and a rescaling. One can use this decomposition for machine learning tasks such as the principal component analysis, which is a technique for dimensionality reduction. (2/5).

As part of the @axiom_xyz's Open Source Program v2, Guillaume, Zhengxun and I developed Halo2-svd, a library which provides functionalities to verify the singular value decomposition (SVD) of a matrix and perform basic linear algebra operations in Halo2. (1/5).