Optimization problems involving the minimization of a finite sum of smooth, possibly non-convex functions arise in numerous applications. To achieve a consensus solution over a network, distributed ...

DUNs can be divided into convex optimization based methods and non-convex optimization based methods. On the one hand, DUNs based on convex optimization algorithms cannot handle non-convex ...

These methods are characterized by high computational efficiency and rapid convergence rates, making them particularly suitable for low-dimensional, continuous, smooth convex optimization problems.

This article is devoted to the distributed convex optimization problem for a class of nonlinear multiagent systems under set constraints. The optimization objective function is composed of the cost ...

These advancements rely heavily on optimization algorithms to train large-scale models for various tasks, including language processing and image classification. At the core of this process lies the ...

The first part of the book covers core concepts of convex sets, convex functions, and related basic definitions that serve understanding convex optimization and its corresponding models.

Abstract In this paper, a modified version of the Classical Lagrange Multiplier method is developed for convex quadratic optimization problems. The method, which is evolved from the first order ...

genetic-algorithm evolutionary-algorithms gradient-descent ant-colony-optimization particle-swarm-optimization convex-functions classical-optimization-methods lagrange-multiplier-method ...