Zobrazeno 1 - 10
of 107
pro vyhledávání: '"Solomonik, Edgar"'
Tensor network contractions are widely used in statistical physics, quantum computing, and computer science. We introduce a method to efficiently approximate tensor network contractions using low-rank approximations, where each intermediate tensor ge
Externí odkaz:
http://arxiv.org/abs/2406.09769
Galerkin-based reduced-order models (G-ROMs) have provided efficient and accurate approximations of laminar flows. In order to capture the complex dynamics of the turbulent flows, standard G-ROMs require a relatively large number of reduced basis fun
Externí odkaz:
http://arxiv.org/abs/2311.03694
Autor:
Irmler, Andreas, Kanakagiri, Raghavendra, Ohlmann, Sebastian T., Solomonik, Edgar, Grüneis, Andreas
We propose an algorithm that aims at minimizing the inter-node communication volume for distributed and memory-efficient tensor contraction schemes on modern multi-core compute nodes. The key idea is to define processor grids that optimize intra-/int
Externí odkaz:
http://arxiv.org/abs/2307.08829
Sparse tensor decomposition and completion are common in numerous applications, ranging from machine learning to computational quantum chemistry. Typically, the main bottleneck in optimization of these models are contractions of a single large sparse
Externí odkaz:
http://arxiv.org/abs/2307.05740
Positive linear programs (LPs) model many graph and operations research problems. One can solve for a $(1+\epsilon)$-approximation for positive LPs, for any selected $\epsilon$, in polylogarithmic depth and near-linear work via variations of the mult
Externí odkaz:
http://arxiv.org/abs/2307.03307
Autor:
Hutter, Edward, Solomonik, Edgar
Performance tuning, software/hardware co-design, and job scheduling are among the many tasks that rely on models to predict application performance. We propose and evaluate low-rank tensor decomposition for modeling application performance. We discre
Externí odkaz:
http://arxiv.org/abs/2210.10184
Graph states play an important role in quantum information theory through their connection to measurement-based computing and error correction. Prior work has revealed elegant connections between the graph structure of these states and their multipar
Externí odkaz:
http://arxiv.org/abs/2209.06320
Autor:
Ma, Linjian, Solomonik, Edgar
This work discusses tensor network embeddings, which are random matrices ($S$) with tensor network structure. These embeddings have been used to perform dimensionality reduction of tensor network structured inputs $x$ and accelerate applications such
Externí odkaz:
http://arxiv.org/abs/2205.13163
Autor:
Singh, Navjot, Solomonik, Edgar
CP decomposition (CPD) is prevalent in chemometrics, signal processing, data mining and many more fields. While many algorithms have been proposed to compute the CPD, alternating least squares (ALS) remains one of the most widely used algorithm for c
Externí odkaz:
http://arxiv.org/abs/2204.07208
State-of-the-art parallel sorting algorithms for distributed-memory architectures are based on computing a balanced partitioning via sampling and histogramming. By finding samples that partition the sorted keys into evenly-sized chunks, these algorit
Externí odkaz:
http://arxiv.org/abs/2204.04599