Sparse matrix computations are prevalent in many scientific and technical applications. In many simulation applications, the solving of the sparse matrix-vector multiplication (SpMV) is critical for ...

Sparse matrix computations are pivotal to advancing high-performance scientific applications, particularly as modern numerical simulations and data analyses demand efficient management of large ...

Real PIM systems can provide high levels of parallelism, large aggregate memory bandwidth and low memory access latency, thereby being a good fit to accelerate the widely-used, memory-bound Sparse ...

In particular, we penalize the likelihood with a lasso penalty on the entries of the covariance matrix. This penalty plays two important roles: it reduces the effective number of parameters, which is ...

For dimensions p that can be much larger than the sample size n, we consider the estimation of sparse vectors θ*. Under matrix uncertainty, the Lasso and Dantzig selector turn out to be extremely ...

Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...

[Vinod Stanur] is working with a mouse input and a microcontroller driven LED matrix. The mouse cursor is tracked inside of a window by Python and the resulting coordinates on the LED grid are ...