Search Results for author: Edgar Solomonik

Found 12 papers, 9 papers with code

Multi-Parameter Performance Modeling via Tensor Completion

1 code implementation18 Oct 2022 Edward Hutter, Edgar Solomonik

We propose and evaluate low rank tensor decomposition for modeling application performance.

Scheduling Tensor Decomposition

Cost-efficient Gaussian Tensor Network Embeddings for Tensor-structured Inputs

no code implementations26 May 2022 Linjian Ma, Edgar Solomonik

We provide a systematic way to design tensor network embeddings consisting of Gaussian random tensors, such that for inputs with more general tensor network structures, both the sketch size (row size of $S$) and the sketching computational cost are low.

Dimensionality Reduction Tensor Decomposition

Alternating Mahalanobis Distance Minimization for Stable and Accurate CP Decomposition

no code implementations14 Apr 2022 Navjot Singh, Edgar Solomonik

Computing these critical points in an alternating manner motivates an alternating optimization algorithm which corresponds to alternating least squares algorithm in the matrix case.

ATD: Augmenting CP Tensor Decomposition by Self Supervision

1 code implementation15 Jun 2021 Chaoqi Yang, Cheng Qian, Navjot Singh, Cao Xiao, M Brandon Westover, Edgar Solomonik, Jimeng Sun

This paper addresses the above challenges by proposing augmented tensor decomposition (ATD), which effectively incorporates data augmentations and self-supervised learning (SSL) to boost downstream classification.

Data Augmentation Dimensionality Reduction +3

MTC: Multiresolution Tensor Completion from Partial and Coarse Observations

1 code implementation14 Jun 2021 Chaoqi Yang, Navjot Singh, Cao Xiao, Cheng Qian, Edgar Solomonik, Jimeng Sun

Our MTC model explores tensor mode properties and leverages the hierarchy of resolutions to recursively initialize an optimization setup, and optimizes on the coupled system using alternating least squares.

Fast and Accurate Randomized Algorithms for Low-rank Tensor Decompositions

no code implementations NeurIPS 2021 Linjian Ma, Edgar Solomonik

Experimental results show that this new ALS algorithm, combined with a new initialization scheme based on randomized range finder, yields up to $22. 0\%$ relative decomposition residual improvement compared to the state-of-the-art sketched randomized algorithm for Tucker decomposition of various synthetic and real datasets.

Distributed-Memory DMRG via Sparse and Dense Parallel Tensor Contractions

1 code implementation10 Jul 2020 Ryan Levy, Edgar Solomonik, Bryan K. Clark

The Density Matrix Renormalization Group (DMRG) algorithm is a powerful tool for solving eigenvalue problems to model quantum systems.

Distributed, Parallel, and Cluster Computing Strongly Correlated Electrons Computational Physics

AutoHOOT: Automatic High-Order Optimization for Tensors

1 code implementation10 May 2020 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 decompositions and tensor networks.

Mathematical Software Numerical Analysis Numerical Analysis

Accelerating Alternating Least Squares for Tensor Decomposition by Pairwise Perturbation

2 code implementations26 Nov 2018 Linjian Ma, Edgar Solomonik

The alternating least squares algorithm for CP and Tucker decomposition is dominated in cost by the tensor contractions necessary to set up the quadratic optimization subproblems.

Numerical Analysis Numerical Analysis

Breaking the 49-Qubit Barrier in the Simulation of Quantum Circuits

4 code implementations16 Oct 2017 Edwin Pednault, John A. Gunnels, Giacomo Nannicini, Lior Horesh, Thomas Magerlein, Edgar Solomonik, Robert Wisnieff

With the current rate of progress in quantum computing technologies, 50-qubit systems will soon become a reality.

Quantum Physics

Scaling betweenness centrality using communication-efficient sparse matrix multiplication

2 code implementations22 Sep 2016 Edgar Solomonik, Maciej Besta, Flavio Vella, Torsten Hoefler

Betweenness centrality (BC) is a crucial graph problem that measures the significance of a vertex by the number of shortest paths leading through it.

Distributed, Parallel, and Cluster Computing Discrete Mathematics Mathematical Software G.1.0; G.2.2

Sparse Tensor Algebra as a Parallel Programming Model

2 code implementations30 Nov 2015 Edgar Solomonik, Torsten Hoefler

Dense and sparse tensors allow the representation of most bulk data structures in computational science applications.

Mathematical Software

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