Abstract: Newton method of optimization is very much useful in machine learning and deep learning optimizations due its order two convergence. The major problems of Newton method of optimization are ...

The bleeding edge: In-memory processing is a fascinating concept for a new computer architecture that can compute operations within the system's memory. While hardware accommodating this type of ...

Abstract: A common goal in evolutionary multi-objective optimization is to find suitable finite-size approximations of the Pareto front of a given multi-objective optimization problem. While many ...

Neural networks have been widely used to solve partial differential equations (PDEs) in different fields, such as biology, physics, and materials science. Although current research focuses on PDEs ...

You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs.

Searching for efficiency in the complex optimization world leads researchers to explore methods that promise rapid convergence without the burdensome computational cost typically associated with ...

Implementation of optimization algorithms in python including: Armijo rule , Conjugated direction , Conjugated gradient , Gradient method , Globally Convergent Newton Method , Quasi Newton Method , ...

Private methods are often used as an implementation detail and are not meant to be accessed directly by the users of a class. The name mangling mechanism in Python makes it difficult to call private ...

Department of Computer Science, Regentropfen College of Applied Sciences, Bolgatanga, Ghana. In the realm of computational mathematics and scientific computing, the choice of software can profoundly ...