This project tackles the problem of finding the minimum of a convex function, solving optimization problems, and exploring various root-finding algorithms. The goal is to implement several numerical ...

This paper considers an unconstrained collaborative optimization of a sum of convex functions, where agents make decisions using local information in the presence of random interconnection topologies.

Analytical and numerical techniques like gradient descent, genetic algorithm, ... to solve a convex unconstrained nonlinear optimization problem from scratchh without using any python library - khe ...

The adaptive cubic regularization algorithm described in Cartis et al. (2009, Adaptive cubic regularisation methods for unconstrained optimization. Part I: motivation, convergence and numerical ...

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 ...

In this paper, we consider the convergence rate of ADMM when applying to the convex optimization problems that the subdifferentials of the underlying functions are piecewise linear multifunctions, ...

Researchers tackled the numerical limitations of Expected Improvement (EI) in Bayesian optimization. Introducing LogEI functions, they demonstrated enhanced numerical stability while surpassing ...