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.

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

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 ...

This article investigates a distributed time-varying optimization problem with inequality constraints, aiming to find finite-time and fixed-time convergent solutions free from initialization. A ...

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 ...

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 ...

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 ...