In this work, we propose a proximal algorithm for unconstrained optimization on the cone of symmetric semidefinite positive matrices. It appears to be the first in the proximal class on the set of methods that convert a Symmetric Definite Positive Optimization in Nonlinear Optimization. It replaces the main iteration of the conceptual proximal point algorithm by a sequence of nonlinear programm...

Mobile cloud computing (MCC) is a new technology that has been developed to overcome the restrictions of smart mobile devices (e.g. battery, processing power, storage capacity, etc.) to send a part of the program (with complex computing) to the cloud server (CS). In this paper, we study a multi-cell with multi-input and multi-output (MIMO) system in which the cell-interior users request service...

The dynamical functional particle method (DFPM) is a method for solving equations by using a damped second order dynamical system. The dynamical system is solved by a symplectic method that is especially tailored for conservative systems. In this work we have extended DFPM to convex optimization problems with constraints. The method is tested on linear eigenvalue problems with normalization and...

This paper deals with the constrained predictive control of nonlinear systems. Artificial Neural Networks (ANN) are used as a process model. The control law is derived by minimizing a non convex criterion. The optimization problem is solved using Ellipsoid and genetic algorithms. The structure and operators of the combining two algorithms have been specifically developed for control design prob...

where c ∈ R, g : R → R is non-linear and smooth on its domain, and Ω is a nonempty closed convex subset in R. This paper introduces sequential convex programming (SCP), a local optimization method for solving the nonconvex problem (P). We prove that under acceptable assumptions the SCP method locally converges to a KKT point of (P) and the rate of convergence is linear. Problems in the form of ...

Here we study non-convex composite optimization: first, a finite-sum of smooth but non-convex functions, and second, a general function that admits a simple proximal mapping. Most research on stochastic methods for composite optimization assumes convexity or strong convexity of each function. In this paper, we extend this problem into the non-convex setting using variance reduction techniques, ...

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