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Conference Paper Simple Optimization Toolbox for Engineers
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Authors
Jae-chan Jeong, Sunglok Choi, Jae-Yeong Lee, Ji-Wan Kim, Jae-il Cho
Issue Date
2015-10
Citation
International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) 2015, pp.103-103
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/URAI.2015.7358973
Project Code
15GC1400, The development of visual surveillance system for safety management of train station, Lee Jae-Yeong
Abstract
Summary form only given. Optimization problem is to find the best solution from all feasible solutions such as camera calibration[1], 3D reconstruction [2], visual odometry[3], human skeleton modeling. In order to find best solution, choosing an object function is very important. According to object function, the optimization problem could be distinguished with singular or multiple variable problem and linear or nonlinear optimization problem. If the optimization problem has special constraints except an object function, the problem is constrained optimization problem in contrast non-constrained optimization problem. In this video paper, simple optimization toolbox (SOT) is made to understand optimization method intuitively[3]. SOT deals with general unconstrained nonlinear optimization problem. SOT is written in interpreted language Matlab/Octave[4]. However, SOT GUI supports only Matlab. Various optimization methods are implemented in SOT such as gradient descent, newton, gradient descent based line search, newton based trust region, newton based trust region with saddle free[5], quasi newton, gauss newton and Levenberg-Marquardt. SOT provides simple examples to understand how to use the optimization method and engineering examples to show how to define and solve engineering problems: line search, scan matching and visual odometry[3]. And SOT supports visualization method such as plot and plot_x. By using theses functions, user could draw object functions and optimization process step by step easily. Figure 1 shows SOT GUI tool. Gradient descent based line search method is selected and test function is rosenbrock[3]. Goal position is (1, 1). User can observe the optimization sequence step by step using step button and modify parameters.
KSP Keywords
3D Reconstruction, Camera Calibration, Constrained optimization problem, Engineering problems, Feasible solution, GUI tool, Human skeleton modeling, Levenberg-marquardt(LM), Line Search, Multiple variable, Nonlinear optimization problem