scholarly journals An alternating linearization bundle method for a class of nonconvex optimization problem with inexact information

2017 ◽  
Vol 13 (5) ◽  
pp. 0-0
Author(s):  
Hui Gao ◽  
◽  
Jian Lv ◽  
Xiaoliang Wang ◽  
Liping Pang ◽  
...  
Keyword(s):  
Nonconvex Optimization ◽  
Optimization Problem ◽  
Bundle Method ◽  
Inexact Information

A proximal bundle method for constrained nonsmooth nonconvex optimization with inexact information

2017 ◽  
Vol 70 (3) ◽  
pp. 517-549 ◽  
Author(s):  
Jian Lv ◽  
Li-Ping Pang ◽  
Fan-Yun Meng
Keyword(s):  
Nonconvex Optimization ◽  
Bundle Method ◽  
Nonsmooth Nonconvex Optimization ◽  
Inexact Information ◽  
Proximal Bundle Method

Genetic search - An approach to the nonconvex optimization problem

AIAA Journal ◽  
1990 ◽  
Vol 28 (7) ◽  
pp. 1205-1210 ◽  
Author(s):  
P. Hajela
Keyword(s):  
Nonconvex Optimization ◽  
Optimization Problem ◽  
Genetic Search

scholarly journals Power Allocation for Reducing PAPR of Artificial-Noise-Aided Secure Communication System

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Tao Hong ◽  
Geng-xin Zhang
Keyword(s):  
Power Allocation ◽  
Nonconvex Optimization ◽  
Communication System ◽  
Secure Communication ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Average Power ◽  
Finite Alphabet ◽  
Artificial Noise ◽  
Communication Signals

The research of improving the secrecy capacity (SC) of wireless communication system using artificial noise (AN) is one of the classic models in the field of physical layer security communication. In this paper, we consider the peak-to-average power ratio (PAPR) problem in this AN-aided model. A power allocation algorithm for AN subspaces is proposed to solve the nonconvex optimization problem of PAPR. This algorithm utilizes a series of convex optimization problems to relax the nonconvex optimization problem in a convex way based on fractional programming, difference of convex (DC) functions programming, and nonconvex quadratic equality constraint relaxation. Furthermore, we also derive the SC of the proposed signal under the condition of the AN-aided model with a finite alphabet and the nonlinear high-power amplifiers (HPAs). Simulation results show that the proposed algorithm reduces the PAPR value of transmit signal to improve the efficiency of HPA compared with benchmark AN-aided secure communication signals in the multiple-input single-output (MISO) model.

Numerical Methods for a Nonconvex Optimization Problem Modeling Martensitic Microstructure

1997 ◽  
Vol 18 (4) ◽  
pp. 1122-1141 ◽  
Author(s):  
Roy A. Nicolaides ◽  
Noel Walkington ◽  
Han Wang
Keyword(s):  
Numerical Methods ◽  
Nonconvex Optimization ◽  
Optimization Problem ◽  
Problem Modeling ◽  
Martensitic Microstructure

A Redistributed Proximal Bundle Method for Nonconvex Optimization

2010 ◽  
Vol 20 (5) ◽  
pp. 2442-2473 ◽  
Author(s):  
Warren Hare ◽  
Claudia Sagastizábal
Keyword(s):  
Nonconvex Optimization ◽  
Bundle Method ◽  
Proximal Bundle Method

Aircraft controller synthesis by solving a nonconvex optimization problem

1995 ◽  
Vol 18 (2) ◽  
pp. 382-384 ◽  
Author(s):  
R. Srichander
Keyword(s):  
Nonconvex Optimization ◽  
Optimization Problem ◽  
Controller Synthesis

scholarly journals Robust strong duality for nonconvex optimization problem under data uncertainty in constraint

2021 ◽  
Vol 6 (11) ◽  
pp. 12321-12338
Author(s):  
Yanfei Chai ◽  
Keyword(s):  
Nonconvex Optimization ◽  
Optimization Problem ◽  
Conjugate Function ◽  
Strong Duality ◽  
Data Uncertainty ◽  
Abstract Convexity ◽  
Conjugate Duality ◽  
Dual Problems ◽  
Uncertain Optimization ◽  
Robust Strong Duality

<abstract><p>This paper deals with the robust strong duality for nonconvex optimization problem with the data uncertainty in constraint. A new weak conjugate function which is abstract convex, is introduced and three kinds of robust dual problems are constructed to the primal optimization problem by employing this weak conjugate function: the robust augmented Lagrange dual, the robust weak Fenchel dual and the robust weak Fenchel-Lagrange dual problem. Characterizations of inequality (1.1) according to robust abstract perturbation weak conjugate duality are established by using the abstract convexity. The results are used to obtain robust strong duality between noncovex uncertain optimization problem and its robust dual problems mentioned above, the optimality conditions for this noncovex uncertain optimization problem are also investigated.</p></abstract>

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