New Convergent Algorithm for Solving a Class of Optimization Problems

A convergent algorithm is proposed for solving a class of optimization problems (P1), which can be broadly applied to engineering designs and stability analysis of nonlinear systems. By utilizing logarithmic characteristic and linearization technique, linear relaxation programming (LRP) about proble...

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Bibliographic Details
Main Authors: Hong-Wei Jiao, Jing-Ben Yin, Kun Li
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
branch and bound
Design engineering
Design optimization
Educational institutions
fractional problem
global optimization
Linear programming
linear technique
Linearization techniques
Mathematics
Nonlinear systems
Optimization methods
Stability analysis
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Summary:A convergent algorithm is proposed for solving a class of optimization problems (P1), which can be broadly applied to engineering designs and stability analysis of nonlinear systems. By utilizing logarithmic characteristic and linearization technique, linear relaxation programming (LRP) about problem (P1) is established, through the successive refinement of the linear relaxation of the feasible region and the solutions of a series of linear relaxation programming (LRP), the proposed algorithm is convergent to the global minimum of the (P1). And finally the numerical results show the feasibility of the proposed algorithm.
ISSN:2160-7443
DOI:10.1109/ICIC.2010.136