This paper proposes a novel transmit power allocation combined with the phase compensation for OFDM system. This technique consists of adapting the power allocation and the phase compensation in the frequency domain depending on the channel variations. Optimization process is based on the optimality of the global Bit Error Rate (BER). Simulation results show significant performance gains can be...

We present a class of methods for distributed optimization with event-triggered communication. To this end, we extend Nesterov’s first order scheme to use event-triggered communication in a networked environment. We then apply this approach to generalize the proximal center algorithm (PCA) for separable convex programs by Necoara and Suykens. Our method uses dual decomposition and applies the d...

To date the primary focus of most constrained approximate optimization strategies is that application of the method should lead to improved designs. Few researchers have focused on the development of constrained approximate optimization strategies that are assured of converging to a Karush-Kuhn-Tucker (KKT) point for the problem. Recent work by the authors based on a trust region model manageme...

This paper proposes a novel algorithm for solving discrete online learning problems under stochastic constraints, where the leaner aims to maximize the cumulative reward given that some additional constraints on the sequence of decisions need to be satisfied on average. We propose Lagrangian exponentially weighted average (LEWA) algorithm, which is a primal-dual variant of the well known expone...

We propose a simple and very effective algorithm for solving the generalized assignment problem exactly. Our contribution is twofold: we reformulate the optimization problem into a sequence of decision problems, and we solve these effectively using variable-fixing rules. The decision problems are solved by a simple depth-first lagrangian branch-and-bound method, improved by the variable-fixing ...

In this paper, “chance optimization” problems are introduced, where one aims at maximizing the probability of a set defined by polynomial inequalities. These problems are, in general, nonconvex and computationally hard. With the objective of developing systematic numerical procedures to solve such problems, a sequence of convex relaxations based on the theory of measures and moments is provided...

the purpose of this paper is to investigate the egm method and the behavior of a solid particle suspended in a twodimensional rectangular cavity due to conjugate natural convection. a thermal lattice boltzmann bgk model is implemented to simulate the two dimensional natural convection and the particle phase was modeled using the lagrangian–lagrangian approach where the solid particles are treat...

We deal with a generalization of the proximal-point method and the closely related Tikhonov regularization method for convex optimization problems. The prime motivation behind this is the well-known connection between the classical proximal-point and augmented Lagrangian methods, and the emergence of modified augmented Lagrangian methods in recent years. Our discussion includes a formal proof o...

This paper presents an active-set algorithm for large-scale optimization that occupies the middle ground between sequential quadratic programming (SQP) and sequential linear-quadratic programming (SL-QP) methods. It consists of two phases. The algorithm first minimizes a piecewise linear approximation of the Lagrangian, subject to a linearization of the constraints, to determine a working set. ...

The asymptotic optimal performance of variable-rate vector quantizers of fixed dimension and large rate was first developed in a rigorous fashion by Paul Zador. Subsequent design algorithms for such compression codes used a Lagrangian formulation in order to generalize Lloyd’s classic quantizer optimization algorithm to variable rate codes. This formulation has been subsequently adopted in a va...