my_convex_optimization uses mathematical optimization algorithms like gradient descent and linear programming to iteratively solve convex problems by minimizing an objective function, either through ...

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.

In this paper, a time-varying distributed convex optimization problem is studied for continuous-time multi-agent systems. The objective is to minimize the sum of local time-varying cost functions, ...

Course Description This course discusses basic convex analysis (convex sets, functions, and optimization problems), optimization theory (linear, quadratic, semidefinite, and geometric programming; ...

ECP is a global optimization algorithm for maximization that minimizes unpromising evaluations by concentrating on potentially optimal regions. It eliminates the need for estimating the Lipschitz ...

Brief Overview A large body of problems in information theory, estimation theory, finance and machine learning can be formulated as max P X∈F G(P X) (∗) max P X ∈ F G (P X) (∗) where G(⋅) G () is some ...

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 ...

The main result proved in this paper is that the ratio of the square of a nonnegative convex function to a strictly positive concave function is convex over a convex domain. Some particular cases of ...