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An algorithm for global solution to bi-parametric linear complementarity constrained linear programs

JOURNAL OF global optimization 2015年第2期62卷 263-297页

作者： Lee, Yu-Ching Pang, Jong-Shi Mitchell, John E. Univ Illinois Dept Ind & Enterprise Syst Engn Urbana IL 61801 USA Univ So Calif Daniel J Epstein Dept Ind & Syst Engn Los Angeles CA 90089 USA Rensselaer Polytech Inst Dept Math Sci Troy NY 12180 USA

A linear program with linear complementarity constraints (LPCC) is among the simplest mathematical programs with complementarity constraints. Yet the global solution of the LPCC remains difficult to find and/or verify. In this work we study a specific type of the LPCC which we term a bi-parametric LPCC. Reformulating the bi-parametric LPCC as a non-convex quadratically constrained program, we develop a domain-partitioning algorithm that solves a series of the linear subproblems and/or convex quadratically constrained subprograms obtained by the relaxations of the complementarity constraint. The choice of an artificial constants-pair allows us to control the domain on which the partitioning is done. Numerical results of robustly solving 105 randomly generated bi-parametric LPCC instances of different structures associated with different numbers of complementarity constraints by the algorithm are presented.

关键词： Mathematical program with complementarity constraints Bi-parametric program Domain partitioning global optimization algorithm

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global resolution of the support vector machine regression parameters selection problem with LPCC

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EURO JOURNAL ON COMPUTATIONAL optimization 2015年第3期3卷 197-261页

作者： Lee, Yu-Ching Pang, Jong-Shi Mitchell, John E. Univ Illinois Urbana IL 61801 USA Univ Southern Calif Los Angeles CA 90089 USA Rensselaer Polytech Inst Troy NY 12180 USA

Support vector machine regression is a robust data fitting method to minimize the sum of deducted residuals of regression, and thus is less sensitive to changes of data near the regression hyperplane. Two design parameters, the insensitive tube size (ee) and the weight assigned to the regression error trading off the normed support vector (Ce), are selected by user to gain better forecasts. The global training and validation parameter selection procedure for the support vector machine regression can be formulated as a bi-level optimization model, which is equivalently reformulated as linear program with linear complementarity constraints (LPCC). We propose a rectangle search global optimization algorithm to solve this LPCC. The algorithm exhausts the invariancy regions on the parameter plane ((Ce, ee)-plane) without explicitly identifying the edges of the regions. This algorithm is tested on synthetic and real-world support vector machine regression problems with up to hundreds of data points, and the efficiency are compared with several approaches. The obtained global optimal parameter is an important benchmark for every other selection of parameters.

关键词： Support vector Machine regression Parameter selection global optimal parameter Mathematical program with complementarity constraints global optimization algorithm

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A Modified Back Propagation algorithm of Neural Network with global optimization

A Modified Back Propagation Algorithm of Neural Network with...

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2014 3rd International Conference on Key Engineering Materials and Computer Science(KEMCS 2014)

作者： Jingjing Li Zhe Cui Chengdu Institute of Computing Technology Chinese Academy of Sciences

The advantages and weakens of traditional BP algorithm is briefly analyzed and an efficient global optimization algorithm is *** basic principle of the algorithm is presented,and a new BP neural network algorithm based on the existing BP algorithm and the new global optimization algorithm is proposed, considering the new global optimization algorithm can solve the problem of local minimum efficiently. To verify the effectiveness of the new BP algorithm,the paper compared the experimental results of various algorithms in solving function fitting problem.

关键词： Neural network BP algorithm global optimization algorithm global minimum Modified algorithm

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optimization of hydrogen distribution network considering pressure and heat recovery 7th

Optimization of hydrogen distribution network considering pr...

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7th International Conference on Applied Energy (ICAE)

作者： Dong, Ruifeng Yu, Yunsong Zhang, Zaoxiao Xi An Jiao Tong Univ State Key Lab Multiphase Flow Power Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Chem Engn & Technol Xian 710049 Peoples R China

Hydrogen is an important resource in chemical processes. In the hydrogen network, many units are operating at high temperature and pressure, which leads to a large energy change of hydrogen streams. However, the recovery of heat and work in the hydrogen flows has seldom been studied together with the optimization of hydrogen network. It is obviously that the reuse of heat and work energy will raise the effective utilization rate of energy. The recovery of heat could be realized by heat exchangers. The recovery of work will be realized by rotary work exchangers, which are composed of several compressors and turbines with the same shaft. A state space superstructure is adopted to handle all the variables. The mathematical model is built based on exergoeconomic analysis considering both energy and economic factors. The optimization problem will be a mixed integer nonlinear programming (MINLP) problem. The existing algorithms will be improved. A typical refinery hydrogen network is studied as an example. The state space superstructure, exergoeconomic analysis and proposed algorithm could solve the problem competently. The consideration of pressure and heat recovery could reduce the energy consumption and economic cost simultaneously.

关键词： Hydrogen network Heat recovery Work recovery Exergoeconomic analysis global optimization algorithm

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Design of URAs by DIRECT global optimization algorithm

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OPTIK 2009年第8期120卷 370-373页

作者： Lang, Haitao Liu, Liren Yang, Qingguo Beijing Univ Chem Technol Beijing 100029 Peoples R China Chinese Acad Sci Shanghai Inst Opt & Fine Mech Shanghai 201800 Peoples R China

Coded aperture imaging (CAI) has evolved as a standard technique for imaging high-energy photon sources and has found numerous applications. Coded aperture arrays (CAAs) are the most important devices in the applications of CAI. In recent years, many approaches were presented to design optimum or near-optimum CAAs. Uniformly redundant arrays (URAs) are the most successful CAAs for their cyclic autocorrelation consisting of a sequence of delta functions on a flat sidelobe, which can easily be subtracted when the object has been reconstructed. Unfortunately, the existing methods can only be used to design URAs with limited number of array sizes and fixed autocorrelative sidelobe-to-peak ratio. In this paper, we presented a method to design more flexible URAs by means of a global optimization algorithm named DIRECT. By our approaches, we obtain various types of URAs including the filled URAs which can be constructed by existing methods and the sparse URAs which have never been constructed and mentioned by existing papers as far as we know. (C) 2007 Elsevier GmbH. All rights reserved.

关键词： Coded aperture arrays Filled URAs Sparse URAs global optimization algorithm DIRECT

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optimization of Hydrogen Distribution Network Considering Pressure and heat recovery

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Energy Procedia 2015年 75卷 1147-1152页

作者： Ruifeng Dong Yunsong Yu Zaoxiao Zhang State Key Laboratory of Multiphase Flow in Power Engineering Xi’an Jiaotong University Xi’an 710049 PR China School of Chemical Engineering and Technology Xi’an Jiaotong University Xi’an 710049 PR China

关键词： Hydrogen network Heat recovery Work recovery Exergoeconomic analysis global optimization algorithm

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optimization for Hazardous Materials Road Transportation based on Multi-objective Method

Optimization for Hazardous Materials Road Transportation bas...

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3rd International Conference on Intelligent System Design and Engineering Applications (ISDEA)

作者： Li, Xiang Jiang, Huiyuan Wuhan Univ Technol Sch Transportat Wuhan 430063 Hubei Peoples R China

ISBN: (纸本)9780769549231

On the basis of the previous related work of hazardous materials road transportation and considered the real problem of hazardous materials management, this paper propose a model based on multi-objective optimization. Transportation risk, route and freight are weighted to get a similar generalized cost expression which makes the function of influencing factors more flexibility and applicability. global optimization algorithm is simple and easy to be calculated which will provide a more operable model for the optimization problem of hazardous material road transportation. Finally, the feasibility of the method is proved by the given example.

关键词： Hazardous Materials Route optimization Multi-objective global optimization algorithm

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Water Quantity Prediction Based on Particle Swarm optimization and Evolutionary algorithm Using Recurrent Neural Networks

Water Quantity Prediction Based on Particle Swarm Optimizati...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhang, Nian Lai, Shuhua Univ Dist Columbia Dept Elect & Comp Engn Washington DC 20008 USA

ISBN: (纸本)9781424496365

Stormwater pollution is one of most important issues that the District of Columbia faces. Urban stormwater pollution can be a large contributor to the water quality problems of many receiving waters, as runoff transports a wide spectrum of pollutants to local receiving waters and their cumulative magnitude is large. Therefore, evaluations of stormwater runoff quantity are necessary to enhance the performance of an assessment operation and develop better water resources management and plan. However, some computational intelligence methods that have most successful applications on time series prediction have not yet been investigated on water quantity prediction. Only a limited number of neural networks models were applied to the water quantity monitoring. Therefore, we proposed an Elman style based recurrent neural network on the water quantity prediction. A hybrid learning algorithm incorporating particle swarm optimization and evolutional algorithm was presented, which takes the complementary advantages of the two global optimization algorithms. The neural networks model was trained by particle swarm optimization and evolutional algorithm to forecast the stormwater runoff discharge. The USGS real-time water data at Four Mile Run station at Alexandria, VA were used as time series input. The excellent experimental results demonstrated that the proposed method provides a suitable prediction tool for the stormwater runoff monitoring.

关键词： Elman style based recurrent neural network Neural networks Particle swarm optimization Prediction algorithms Rivers Time series analysis Water pollution Water resources computational intelligence method cumulative magnitude environmental science computing evolutionary algorithm evolutionary computation global optimization algorithm hybrid learning algorithm hydrology learning (artificial intelligence) particle swarm optimisation particle swarm optimization recurrent neural nets stormwater pollution stormwater runoff monitoring stormwater runoff quantity time series time series prediction water pollution water quality water quality water quantity monitoring water quantity prediction water resources water resources management

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Model parameter estimation for Channelrhodopsin-2 light gated ion channels

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BMC Neuroscience 2011年第1期12卷 1-2页

作者： Shivakeshavan G Ratnadurai Pramod P Khargonekar Paul R Carney Sachin S Talathi Department of Electrical and Computer Engineering University of Florida Gainesville Florida 32611 USA Department of Biomedical Engineering University of Florida Gainesville Florida 32611 USA Department of Pedriatics University of Florida Gainesville Florida 32611 USA

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Design of coded aperture arrays by means of a global optimization algorithm

Design of coded aperture arrays by means of a global optimiz...

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Conference on Infrared and Photoelectronic Imagers and Detector Devices

作者： Lang, Haitao Liu, Liren Yang, Qingguo Chinese Acad Sci Shanghai Inst Opt & Fine Mech POB 800-211 Shanghai 201800 Peoples R China

ISBN: (纸本)0819463736

Coded aperture imaging (CAI) has evolved as a standard technique for imaging high energy photon sources and has found numerous applications. Coded aperture arrays (CAAs) are the most important devices in the applications of CAI. In recent years, many approaches were presented to design optimum or near-optimum CAAs. Uniformly redundant arrays (URAs) are the most successful CAAs for their cyclic autocorrelation consisting of a sequence of delta functions on a flat sidelobe which can easily be subtracted when the object has been reconstructed. Unfortunately, the existing methods can only be used to design URAs with limited number of array sizes and fixed autocorrelative sidelobe-to-peak ratio. In this paper, we presented a method to design more flexible URAs by means of a global optimization algorithm named DIRECT. By our approaches, we obtain various types of URAs including the filled URAs which can be constructed by existing methods and the sparse URAs which never be constructed and mentioned by existing papers as far as we know.

关键词： coded aperture arrays filled URAs sparse URAs global optimization algorithm DIRECT

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