Convex optimisation constitutes a fundamental area in applied mathematics where the objective is to identify the minimum of a convex function subject to a set of convex constraints. This framework ...

This study introduced an efficient method for solving non-linear equations. Our approach enhances the traditional spectral conjugate gradient parameter, resulting in significant improvements in the ...

This repository contains two projects exploring numerical optimization techniques applied to different problems. The first project focuses on Newton’s method for solving equilibrium conditions, while ...

Abstract: Convex optimization problems are very popular in the VLSI design society due to their guaranteed convergence to a global optimal point. Table data is often fitted into analytical forms like ...

In this note, we extend the algorithms Extra [13] and subgradient-push [10] to a new algorithm ExtraPush for consensus optimization with convex differentiable objective functions over a directed ...

Abstract: Ensuring the safety of dynamical systems is crucial, where collision avoidance is a primary concern. Recently, control barrier functions (CBFs) have emerged as an effective method to ...

SIAM Journal on Numerical Analysis, Vol. 54, No. 2 (2016), pp. 1229-1262 (34 pages) This paper is devoted to the study of finite element approximations to parabolic optimal control problems with ...

We study a class of optimization problems motivated by automating the design and update of AI systems like coding assistants, robots, and copilots. AutoDiff frameworks, like PyTorch, enable efficient ...