The generation of electricity from heat, also known as thermoelectric energy conversion, has proved to be advantageous for ...

We devise a novel quasi-Newton algorithm for solving unconstrained convex optimization problems. The proposed algorithm is built on our previous framework of the iteratively preconditioned ...

This article presents a novel efficient method for gridless line spectrum estimation problems with single snapshot and sparse signals, namely the gradient descent least-squares (GDLS) method.

This comprehensive article explores the foundational optimization technique, Gradient Descent (GD), and its pivotal role in computational mathematics and machine learning (ML). From the basics of ...

In the NeurIPS 2022 Outstanding Paper Gradient Descent: The Ultimate Optimizer, MIT CSAIL and Meta researchers present a novel technique that enables gradient descent optimizers such as SGD and Adam ...