-
```
Hi,
I am using the Conjugate Gradient Solver cg.cu to solve A*x=b.
I changed it a little bit so that it could initialize the vector b from reading
a matrix market file as well. It works fine wit…
-
```
Hi,
I am using the Conjugate Gradient Solver cg.cu to solve A*x=b.
I changed it a little bit so that it could initialize the vector b from reading
a matrix market file as well. It works fine wit…
-
```
Hi,
I am using the Conjugate Gradient Solver cg.cu to solve A*x=b.
I changed it a little bit so that it could initialize the vector b from reading
a matrix market file as well. It works fine wit…
-
```
Hi,
I am using the Conjugate Gradient Solver cg.cu to solve A*x=b.
I changed it a little bit so that it could initialize the vector b from reading
a matrix market file as well. It works fine wit…
-
IPLS breaks for not definite positive covariance matrix error using AE measurement model in scenarios with measurement gaps. The error happens at the second iteration of IPLS (the one after UKF smooth…
-
> It's so much better to extract the basic knowledge from Precalculus in here.
## Prerequisites
- [x] Precalculus:
- [x] Trigonometry: Unit circle, Inverse trig function
- [x] Geometry: …
-
JAXsim currently focuses only on sampling performance, exploiting `jax.jit` and `jax.vmap`. Being written in JAX, the forward step of the simulation should be differentiable (also considering contact …
-
This might be a meta flag for Gaussian nodes.
For example by default we might assume Hermittian structure and use `meta = AssumeHermittian()` that fallbacks to `fastcholesky`.
```julia
cholinv(:…
-
There are a few linear solvers that are staples in inverse problems and that we, if nothing else, should have for reference.
These include
- [ ] #885. The [Generalized minimal residual](https:/…
-
# 🚀 Feature Request
Represent [TN, TM] tensors by TxT blocks of NxM lazy tensors. While block matrices are supported, the efficient representation is only when there is a diagonal structure over th…