Stochastic Optimization: Theory and Applications

Bin Wang,Neal N Xiong,Rung Ching Chen,Lotfollah Najjar

doi:10.1155/2013/949131

Abstract

1 Engineering Optimization and Smart Antenna Institute, School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Hebei, Qinhuangdao 066004, China 2 Information System Quantitative Analysis, College of Information Science & Technology (IS&T), University of Nebraska at Omaha, Omaha, NE 68182, USA 3 School of Computer Science, Colorado Technical University, 4435 North Chestnut Street, Colorado Springs, CO 80907, USA 4College of Informatics Chaoyang University of Technology, 168 Jifong East Road, Wufong District, Taichung City 41349, Taiwan

Highlights

Biological, engineering, and economic problems can be viewed as stochastic systems, for example, area of communication, gene, signal processing, geography, civil engineering, aerospace, banking, and so forth
In the paper entitled “Qualitative and quantitative integrated modeling for stochastic simulation and optimization,” the authors propose a qualitative and quantitative combined modeling specification based on a hierarchical model structure framework
The new modeling approach is based on a hierarchical model structure which includes the meta-meta model, the metamodel, and the high-level model

Summary

Introduction

In the paper entitled “Qualitative and quantitative integrated modeling for stochastic simulation and optimization,” the authors propose a qualitative and quantitative combined modeling specification based on a hierarchical model structure framework. In the paper entitled “Doubly constrained robust blind beamforming algorithm,” the authors propose doubly constrained robust least squares constant modulus algorithm (LSCMA) to solve the problem of signal steering vector mismatches via the Bayesian method and worst-case performance optimization, which is based on the mismatches between the actual and presumed steering vectors.

Results

Conclusion