Fix a few typos in README.md

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.