1 citations · 1 across the 2 of their papers we have counts for
9 papers
Cost-efficient Gaussian Tensor Network Embeddings for Tensor-structured Inputs
Linjian Ma, Edgar Solomonik
This work discusses tensor network embeddings, which are random matrices () with tensor network structure. These embeddings have been used to perform dimensionality reduction of…
Low Rank Approximation in Simulations of Quantum Algorithms
Linjian Ma, Chao Yang
Simulating quantum algorithms on classical computers is challenging when the system size, i.e., the number of qubits used in the quantum algorithm, is moderately large. However, so…
Fast and Accurate Randomized Algorithms for Low-rank Tensor Decompositions
Linjian Ma, Edgar Solomonik
Low-rank Tucker and CP tensor decompositions are powerful tools in data analytics. The widely used alternating least squares (ALS) method, which solves a sequence of over-determine…
Efficient parallel CP decomposition with pairwise perturbation and multi-sweep dimension tree
Linjian Ma, Edgar Solomonik
CP tensor decomposition with alternating least squares (ALS) is dominated in cost by the matricized-tensor times Khatri-Rao product (MTTKRP) kernel that is necessary to set up the…
AutoHOOT: Automatic High-Order Optimization for Tensors
Linjian Ma, Jiayu Ye, Edgar Solomonik
High-order optimization methods, including Newton's method and its variants as well as alternating minimization methods, dominate the optimization algorithms for tensor decompositi…
Comparison of Accuracy and Scalability of Gauss-Newton and Alternating Least Squares for CP Decomposition
Navjot Singh, Linjian Ma, Hongru Yang +1
Alternating least squares is the most widely used algorithm for CP tensor decomposition. However, alternating least squares may exhibit slow or no convergence, especially when high…