scholarly journals Convex Optimization Theory Applied to Joint Transmitter-Receiver Design in MIMO Channels

2005 ◽  
pp. 269-317 ◽  
Author(s):  
Daniel Pérez Palomar ◽  
Antonio Pascual-Iserte ◽  
John M. Cioffi ◽  
Miguel Angel Lagunas
Keyword(s):  
Convex Optimization ◽  
Optimization Theory ◽  
Receiver Design ◽  
Mimo Channels

Balancing of Flexible Rotors Using Convex Optimization Techniques: Optimum Min-Max LMI Influence Coefficient Balancing

2008 ◽  
Vol 130 (2) ◽  
Author(s):  
Costin D. Untaroiu ◽  
Paul E. Allaire ◽  
William C. Foiles
Keyword(s):  
Convex Optimization ◽  
Optimization Theory ◽  
Numerical Algorithms ◽  
Optimization Methods ◽  
Industrial Applications ◽  
Optimization Techniques ◽  
Linear Matrix ◽  
Influence Coefficient ◽  
Flexible Rotors ◽  
Optimum Solution

In some industrial applications, influence coefficient balancing methods fail to find the optimum vibration reduction due to the limitations of the least-squares optimization methods. Previous min-max balancing methods have not included practical constraints often encountered in industrial balancing. In this paper, the influence coefficient balancing equations, with suitable constraints on the level of the residual vibrations and the magnitude of correction weights, are cast in linear matrix inequality (LMI) forms and solved with the numerical algorithms developed in convex optimization theory. The effectiveness and flexibility of the proposed method have been illustrated by solving two numerical balancing examples with complicated requirements. It is believed that the new methods developed in this work will help in reducing the time and cost of the original equipment manufacturer or field balancing procedures by finding an optimum solution of difficult balancing problems. The resulting method is called the optimum min-max LMI balancing method.

A Novel Convex Optimization for Receive Antenna Selection

2011 ◽  
Vol 186 ◽  
pp. 611-615
Author(s):  
Yong Wang ◽  
Hui Li
Keyword(s):  
Convex Optimization ◽  
Antenna Selection ◽  
Linear Equations ◽  
Multiple Input Multiple Output ◽  
Optimal Choice ◽  
Mimo Channels ◽  
Newton Step ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Receive Antenna

This paper proposes a new receive antenna selection algorithm based on the theory of convex optimization that improve the system performance over Rayleigh fading multiple-input multiple-output (MIMO) channels. The algorithm is based on approximated relaxed original optimization problem. The main effort in the approximated relaxed method is computing the Newton step for the centering problem, which consists of solving sets of linear equations constraints. The method produces not only a suboptimal choice of receive antennas, but also, a bound on how well the globally optimal choice does. The Monte-Carlo simulations show that the algorithm proposed can provide the performance very close to that of the optimal selection based on exhaustive search.

scholarly journals Iterative approximation of solution of split variational inclusion problem

Filomat ◽  
2018 ◽  
Vol 32 (8) ◽  
pp. 2921-2932 ◽  
Author(s):  
Jeremiah Ezeora ◽  
Chinedu Izuchukwu
Keyword(s):  
Convex Optimization ◽  
Hilbert Spaces ◽  
Optimization Problems ◽  
Optimization Theory ◽  
Variational Problems ◽  
Variational Inclusion ◽  
Inclusion Problem ◽  
Iterative Approximation ◽  
Convex Optimization Problems ◽  
Variational Inclusion Problem

Following recent important results of Moudafi [Journal of Optimization Theory and Applications 150(2011), 275-283] and other related results on variational problems, we introduce a new iterative algorithm for approximating a solution of monotone variational inclusion problem involving multi-valued mapping. The sequence of the algorithm is proved to converge strongly in the setting of Hilbert spaces. As application, we solved split convex optimization problems.

Sign in / Sign up

Export Citation Format

Share Document