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.

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 ...

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; ...

This paper studies a distributed composite convex optimization problem for multi-agent systems over an unbalanced directed graph. The global objective function is the sum of local cost functions with ...

Gerald Beer, Conjugate Convex Functions and the Epi-Distance Topology, Proceedings of the American Mathematical Society, Vol. 108, No. 1 (Jan., 1990), pp. 117-126 ...

In this paper we are concerned with the problem of finding the global minimum of a concave function over a closed, convex, possibly unbounded set in R n. The intrinsic difficulty of this problem is ...