From 6e7a5124800298cbd9c75224e13bdd9b14629d00 Mon Sep 17 00:00:00 2001 From: Eric Phipps Date: Wed, 3 Jul 2024 13:04:50 -0600 Subject: [PATCH] Fix a few typos in README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index bf7102f300..88a4244012 100644 --- a/README.md +++ b/README.md @@ -14,9 +14,9 @@ Government retains certain rights in this software. GenTen is a tool for computing Canonical Polyadic (CP, also called CANDECOMP/PARAFAC) decompositions of tensor data. It is geared towards analysis of extreme-scale data and implements several CP decomposition algorithms that are parallel and scalable, including: * CP-ALS: The workhorse algorithm for Gaussian sparse or dense tensor data. -* [CP-OPT](https://doi.org/10.1002/cem.1335): CP decomposition of Gaussian data using a quasi-Newton optimization algorithm incorporating possible upper and lower bound constraints. +* [CP-OPT](https://doi.org/10.1002/cem.1335): CP decomposition of (sparse or dense) Gaussian data using a quasi-Newton optimization algorithm incorporating possible upper and lower bound constraints. * [GCP](https://epubs.siam.org/doi/abs/10.1137/18M1203626): Generalized CP supporting arbitrary loss functions (Gaussian, Poisson, Bernoulli, ...), solved using [quasi-Newton](https://epubs.siam.org/doi/abs/10.1137/18M1203626) (dense tensors) or [stochastic gradient descent](https://doi.org/10.1137/19M1266265) (sparse or dense tensors) optimization methods. -* [Streaming GCP](https://doi.org/10.1145/3592979.3593405): A GCP algorithm that incrementally updates a GCP decomposition as new data is observed, suitable for analysis of in situ analysis of streaming data. +* [Streaming GCP](https://doi.org/10.1145/3592979.3593405): A GCP algorithm that incrementally updates a GCP decomposition as new data is observed, suitable for in situ analysis of streaming data. * Federated GCP: A federated learning algorithm for GCP supporting asynchronous parallel communication. GenTen does not provide CP-APR for Poisson data (see [SparTen](https://github.com/sandialabs/sparten) instead) nor other tensor decompositions methods such as Tucker (see [TuckerMPI](https://gitlab.com/tensors/TuckerMPI) instead) or Tensor Train.