PEPSKit.jl is a new Julia software package providing high-level algorithms for iPEPS tensor-network simulations of 2D quantum systems with symmetry support.
Title resolution pending
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
representative citing papers
Random orthonormal matrices are minimax optimal for sketched least squares and rotation-invariant embeddings for randomized SVD, yielding the sharpest error bounds.
Accelerates the power method for extracting top principal components using fast sketching and regularized spectral approximation for stronger low-rank guarantees.
Combines polynomial codes and randomized sketching into approximate distributed schemes that mitigate stragglers during optimization and machine learning tasks.
A unified randomized batch-sampling Kaczmarz framework yields scale-invariant expected linear convergence bounds for block methods solving linear systems.
Flexible GMRES stabilizes sketched GMRES through a new residual bound, producing a practical randomized solver with minimal tuning and robust non-increasing residual norms.
Direct SVD solves coupled decompositions; randomized versions with novel balanced subspace selection improve efficiency and apply to face recognition.
citing papers explorer
-
PEPSKit.jl: A Julia package for projected entangled-pair state simulations
PEPSKit.jl is a new Julia software package providing high-level algorithms for iPEPS tensor-network simulations of 2D quantum systems with symmetry support.
-
Sharp analysis of sketched least squares and randomized low-rank approximation
Random orthonormal matrices are minimax optimal for sketched least squares and rotation-invariant embeddings for randomized SVD, yielding the sharpest error bounds.
-
Accelerating Power Method with Fast Sketching for Stronger Low-Rank Approximation
Accelerates the power method for extracting top principal components using fast sketching and regularized spectral approximation for stronger low-rank guarantees.
-
Approximate Distributed Coded Computing: Polynomial Codes and Randomized Sketching
Combines polynomial codes and randomized sketching into approximate distributed schemes that mitigate stragglers during optimization and machine learning tasks.
-
Randomized batch-sampling Kaczmarz methods for solving linear systems
A unified randomized batch-sampling Kaczmarz framework yields scale-invariant expected linear convergence bounds for block methods solving linear systems.
-
Stabilizing randomized GMRES through flexible GMRES
Flexible GMRES stabilizes sketched GMRES through a new residual bound, producing a practical randomized solver with minimal tuning and robust non-increasing residual norms.
-
Randomized coupled decompositions
Direct SVD solves coupled decompositions; randomized versions with novel balanced subspace selection improve efficiency and apply to face recognition.