Abstract: Efficiently synthesizing an entire application that consists of multiple algorithms for hardware implementation is a very difficult and unsolved problem. One of the main challenges is the ...

In order to find the minimizer of Ⅼ using gradient descent with fixed stepsize, we create a function called gd. This function takes the arguments: start, f, gradient, step_size, maxiter, and tolerance ...

Come Saturday morning, a new era of Wisconsin high school football will truly begin when the WIAA playoff bracket is released. Unlike in years past, there is a new qualification method called the ...

Dozens of machine learning algorithms require computing the inverse of a matrix. Computing a matrix inverse is conceptually easy, but implementation is one of the most challenging tasks in numerical ...

Discovering faster algorithms for matrix multiplication remains a key pursuit in computer science and numerical linear algebra. Since the pioneering contributions of Strassen and Winograd in the late ...

Abstract: Matrix-matrix multiplication is one of the most important kernel in linear algebra operations with a multitude of applications in scientific and engineering computing. Sparse matrix ...

There's a reason Adrienne Adhami is able to run marathons, write books, host podcasts and be a keynote speaker to industry-leading companies like Microsoft and Spotify: she's able to make good ...

Katie has a PhD in maths, specializing in the intersection of dynamical systems and number theory. She reports on topics from maths and history to society and animals. Katie has a PhD in maths, ...