2020/05/10 by Linjian Ma, Jiayu Ye, Ma, Linjian +3
Computer Science · Engineering · Mathematics · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Mathematics #Fluid Dynamics and Vibration Analysis #Mathematical Software (cs.MS) #Model Reduction and Neural Networks #Numerical Analysis (math.NA) #Tensor decomposition and applications #cs.MS #cs.NA #math.NA
paper · pdf · doi:10.48550/arxiv.2005.04540
18 pages, 8 figures
openalex publication_date 2020/05/10 · arxiv created 2020/12/24 · arxiv updated 2020/12/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
High-order optimization methods, including Newton's method and its variants as well as alternating minimization methods, dominate the optimization algorithms for tensor decompositions and tensor networks. These tensor methods are used for data analysis and simulation of quantum systems. In this work, we introduce AutoHOOT, the first automatic differentiation (AD) framework targeting at high-order optimization for tensor computations. AutoHOOT takes input tensor computation expressions and generates optimized derivative expressions. In particular, AutoHOOT contains a new explicit Jacobian / Hessian expression generation kernel whose outputs maintain the input tensors' granularity and are easy to optimize. The expressions are then optimized by both the traditional compiler optimization techniques and specific tensor algebra transformations. Experimental results show that AutoHOOT achieves competitive CPU and GPU performance for both tensor decomposition and tensor network applications compared to existing AD software and other tensor computation libraries with manually written kernels. The tensor methods generated by AutoHOOT are also well-parallelizable, and we demonstrate good scalability on a distributed memory supercomputer.