• Title/Summary/Keyword: Trust Region (TR)

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A Trust-Region ICA algorithm (Trust-Region ICA 알고리듬)

  • Park, Heeyoul;Kim, Sookjeong;Park, Seungjin
    • Proceedings of the Korean Information Science Society Conference
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    • 2004.04b
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    • pp.721-723
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    • 2004
  • A trust-region method is a quite attractive optimization technique. It is, in general, faster than the steepest descent method and is free of a learning rate unlike the gradient-based methods. In addition to its convergence property (between linear and quadratic convergence), ifs stability is always guaranteed, in contrast to the Newton's method. In this paper, we present an efficient implementation of the maximum likelihood independent component analysis (ICA) using the trust-region method, which leads to trust-region-based ICA (TR-ICA) algorithms. The useful behavior of our TR-ICA algorithms is confimed through numerical experimental results.

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Algorithm for stochastic Neighbor Embedding: Conjugate Gradient, Newton, and Trust-Region

  • Hongmo, Je;Kijoeng, Nam;Seungjin, Choi
    • Proceedings of the Korean Information Science Society Conference
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    • 2004.10b
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    • pp.697-699
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    • 2004
  • Stochastic Neighbor Embedding(SNE) is a probabilistic method of mapping high-dimensional data space into a low-dimensional representation with preserving neighbor identities. Even though SNE shows several useful properties, the gradient-based naive SNE algorithm has a critical limitation that it is very slow to converge. To overcome this limitation, faster optimization methods should be considered by using trust region method we call this method fast TR SNE. Moreover, this paper presents a couple of useful optimization methods(i.e. conjugate gradient method and Newton's method) to embody fast SNE algorithm. We compared above three methods and conclude that TR-SNE is the best algorithm among them considering speed and stability. Finally, we show several visualizing experiments of TR-SNE to confirm its stability by experiments.

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Optimization of Triple Response Systems by Using the Dual Response Approach and the Hooke-Jeeves Search Method

  • Fan, Shu-Kai S.;Huang, Chia-Fen;Chang, Ko-Wei;Chuang, Yu-Chiang
    • Industrial Engineering and Management Systems
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    • v.9 no.1
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    • pp.10-19
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    • 2010
  • This paper presents an extended computing procedure for the global optimization of the triple response system (TRS) where the response functions are nonconvex (nonconcave) quadratics and the input factors satisfy a radial region of interest. The TRS arising from response surface modeling can be approximated using a nonlinear mathematical program involving one primary (objective) function and two secondary (constraints) functions. An optimization algorithm named triple response surface algorithm (TRSALG) is proposed to determine the global optimum for the nondegenerate TRS. In TRSALG, the Lagrange multipliers of target (secondary) functions are computed by using the Hooke-Jeeves search method, and the Lagrange multiplier of the radial constraint is located by using the trust region (TR) method at the same time. To ensure global optimality that can be attained by TRSALG, included is the means for detecting the degenerate case. In the field of numerical optimization, as the family of TR approach always exhibits excellent mathematical properties during optimization steps, thus the proposed algorithm can guarantee the global optimal solution where the optimality conditions are satisfied for the nondegenerate TRS. The computing procedure is illustrated in terms of examples found in the quality literature where the comparison results with a gradient-based method are used to calibrate TRSALG.

Electromagnetic design and optimization of the multi-segment dielectric-loaded accelerating tube using genetic algorithm

  • M. Nikbakht;H. Afarideh;M. Ghergherehchi
    • Nuclear Engineering and Technology
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    • v.54 no.12
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    • pp.4625-4635
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    • 2022
  • A low-energy dielectric loaded accelerator with a non-uniform, multi-segment structure is studied and optimized. So far, no analytical solution is provided for such structures. Also, due to the existing nonlinear behavior and a large number of geometric parameters, the problem of numerical optimizations is complex. For this reason, a method is presented to design and optimize such structures using the Genetic Algorithm (GA). Moreover, the GA output results are compared with Trust Region (TR) and Nelder-Mead Simplex (NMS) methods. Comparative results show that the GA is more efficient in achieving optimization goals and also has a higher speed than the two other methods. Finally, an optimized accelerating tube is integrated into a proper coupler. Then, the accelerator is simulated for full electromagnetic investigations using the CST suite of codes. This design leads to a structure with a power of about 80 kW in the X-band, which delivers electrons to the output energy in the range of 300-459 kV. The length and outer diameter of the accelerating tube obtained are 10 cm and 1 cm, respectively.