• Title/Summary/Keyword: Line search method

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GLOBAL CONVERGENCE OF AN EFFICIENT HYBRID CONJUGATE GRADIENT METHOD FOR UNCONSTRAINED OPTIMIZATION

  • Liu, Jinkui;Du, Xianglin
    • Bulletin of the Korean Mathematical Society
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    • v.50 no.1
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    • pp.73-81
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    • 2013
  • In this paper, an efficient hybrid nonlinear conjugate gradient method is proposed to solve general unconstrained optimization problems on the basis of CD method [2] and DY method [5], which possess the following property: the sufficient descent property holds without any line search. Under the Wolfe line search conditions, we proved the global convergence of the hybrid method for general nonconvex functions. The numerical results show that the hybrid method is especially efficient for the given test problems, and it can be widely used in scientific and engineering computation.

Waveform Detection Algorithm based on the Search of Distinctive Line-Segments (검색에 기초한 파형 검출 알고리듬)

  • 박승훈;장태규
    • Journal of Biomedical Engineering Research
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    • v.14 no.3
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    • pp.265-272
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    • 1993
  • We present a new waveform detection method, based on the search of distinctive line-segments. The method is based on the basic assumption that the waveform morphology of biological signals is readily characterized by a sequence of the distinctive line-segments and their structural features. In this method, the distinctive line-segments are first searched for, and a structural feature analysis is performed an the distinctive line-segments found. Experiments of detecting epileptic spikes were carried out to evaluate the detection per formance of the method. Two subjects were used for training and tuning the algorithm and four subjects for testing the method. The results were obtained on two different performance indices, detection ratio and the number of false detections per minute.

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Heuristics Method for Sequencing Mixed Model Assembly Lines with Hybridworkstation (혼합작업장을 고려한 혼합모델 조립라인의 투입순서결정에 관한 탐색적기법)

  • 김정자;김상천;공명달
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.21 no.48
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    • pp.299-310
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    • 1998
  • Actually mixed assembly line is mixed with open and close type workstation. This workstation is called hybridworkstation. The propose of this paper is to determine the sequencing of model that minimize line length for actual(hybridworkstation) mixed model assembly line. we developed three mathematical formulation of the problem to minimize the overall length of a line with hybrid station. Mathematical formulation classified model by operato schedule. Mixed model assembly line is combination program and NP-hard program. Thus computation time is often a critical factor in choosing a method of determining the sequence. This study suggests a tabu search technique which can provide a near optimal solution in real time and use the hill climbing heuristic method for selecting initial solution. Modified tabu search method is compared with MIP(Mixed Integer Program). Numerical results are reported to demonstrate the efficiency of the method.

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Dynamic response optmization using approximate search (근사 선탐색을 이용한 동적 반응 최적화)

  • Kim, Min-Soo;Choi, Dong-hoon
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.22 no.4
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    • pp.811-825
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    • 1998
  • An approximate line search is presented for dynamic response optimization with Augmented Lagrange Multiplier(ALM) method. This study empolys the approximate a augmented Lagrangian, which can improve the efficiency of the ALM method, while maintaining the global convergence of the ALM method. Although the approximate augmented Lagragian is composed of only the linearized cost and constraint functions, the quality of this approximation should be good since an approximate penalty term is found to have almost second-order accuracy near the optimum. Typical unconstrained optimization algorithms such as quasi-Newton and conjugate gradient methods are directly used to find exact search directions and a golden section method followed by a cubic polynomial approximation is empolyed for approximate line search since the approximate augmented Lagrangian is a nonlinear function of design variable vector. The numberical performance of the proposed approach is investigated by solving three typical dynamic response optimization problems and comparing the results with those in the literature. This comparison shows that the suggested approach is robust and efficient.

Development of an Efficient Line Search Method by Using the Sequential Polynomial Approximation (순차적 다항식 근사화를 적용한 효율적 선탐색기법의 개발)

  • 김민수;최동훈
    • Transactions of the Korean Society of Mechanical Engineers
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    • v.19 no.2
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    • pp.433-442
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    • 1995
  • For the line search of a multi-variable optimization, an efficient algorithm is presented. The algorithm sequentially employs several polynomial approximations such as 2-point quadratic interpolation, 3-point cubic interpolation/extrapolation and 4-point cubic interpolation/extrapolation. The order of polynomial function is automatically increased for improving the accuracy of approximation. The method of approximation (interpolation or extrapolation) is automatically switched by checking the slope information of the sample points. Also, for selecting the initial step length along the descent vector, a new approach is presented. The performance of the proposed method is examined by solving typical test problems such as mathematical problems, mechanical design problems and dynamic response problems.

EEG WAVEFORM DETECTION BASED ON THE SEARCH OF DISTINCTIVE LINE-SEGMENTS (특징적인 직선요소들의 검색에 기초한 EEG 파형 검출)

  • Park, Seung-Hun;Chang, Tae-Kyu
    • Proceedings of the KOSOMBE Conference
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    • v.1992 no.05
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    • pp.121-122
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    • 1992
  • We present a new EEG waveform detection method, based on the search of distinctive line-segments. The method is based on the assumption that EEG waveform morphology is characterized by a sequence of its distinctive line-segments and their structural features. In this method, the distinctive line segments are first searched for, and the structural feature analysis is performed on the found line-segment sequence. Experiments of detecting epileptic spikes are performed on four different subjects.

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MODIFIED LIMITED MEMORY BFGS METHOD WITH NONMONOTONE LINE SEARCH FOR UNCONSTRAINED OPTIMIZATION

  • Yuan, Gonglin;Wei, Zengxin;Wu, Yanlin
    • Journal of the Korean Mathematical Society
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    • v.47 no.4
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    • pp.767-788
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    • 2010
  • In this paper, we propose two limited memory BFGS algorithms with a nonmonotone line search technique for unconstrained optimization problems. The global convergence of the given methods will be established under suitable conditions. Numerical results show that the presented algorithms are more competitive than the normal BFGS method.

Finding Rectilinear(L1), Link Metric, and Combined Shortest Paths with an Intelligent Search Method (지능형 최단 경로, 최소 꺾임 경로 및 혼합형 최단 경로 찾기)

  • Im, Jun-Sik
    • The Transactions of the Korea Information Processing Society
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    • v.3 no.1
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    • pp.43-54
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    • 1996
  • This paper presents new heuristic search algorithms for searching rectilinear r(L1), link metric, and combined shortest paths in the presence of orthogonal obstacles. The GMD(GuidedMinimum Detour) algorithm combines the best features of maze-running algorithms and line-search algorithms. The SGMD(Line-by-Line GuidedMinimum Detour)algorithm is a modiffication of the GMD algorithm that improves efficiency using line-by-line extensions. Our GMD and LGMD algorithms always find a rectilinear shortest path using the guided A search method without constructing a connection graph that contains a shortest path. The GMD and the LGMD algorithms can be implemented in O(m+eloge+NlogN) and O(eloge+NlogN) time, respectively, and O(e+N) space, where m is the total number of searched nodes, is the number of boundary sides of obstacles, and N is the total number of searched line segment. Based on the LGMD algorithm, we consider not only the problems of finding a link metric shortest path in terms of the number of bends, but also the combined L1 metric and Link Metric shortest path in terms of the length and the number of bands.

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A modified JFNK with line search method for solving k-eigenvalue neutronics problems with thermal-hydraulics feedback

  • Lixun Liu;Han Zhang;Yingjie Wu;Baokun Liu;Jiong Guo;Fu Li
    • Nuclear Engineering and Technology
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    • v.55 no.1
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    • pp.310-323
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    • 2023
  • The k-eigenvalue neutronics/thermal-hydraulics coupling calculation is a key issue for reactor design and analysis. Jacobian-free Newton-Krylov (JFNK) method, featured with super-linear convergence rate and high efficiency, has been attracting more and more attention to solve the multi-physics coupling problem. However, it may converge to the high-order eigenmode because of the multiple solutions nature of the k-eigenvalue form of multi-physics coupling issue. Based on our previous work, a modified JFNK with a line search method is proposed in this work, which can find the fundamental eigenmode together with thermal-hydraulics feedback in a wide range of initial values. In detail, the existing modified JFNK method is combined with the line search strategy, so that the intermediate iterative solution can avoid a sudden divergence and be adjusted into a convergence basin smoothly. Two simplified 2-D homogeneous reactor models, a PWR model, and an HTR model, are utilized to evaluate the performance of the newly proposed JFNK method. The results show that the performance of this proposed JFNK is more robust than the existing JFNK-based methods.

A Branch-and-Bound Algorithm for U-line Line Balancing (U라인 라인밸런싱을 위한 분지한계법)

  • 김여근;김재윤;김동묵;송원섭
    • Journal of the Korean Operations Research and Management Science Society
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    • v.23 no.2
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    • pp.83-101
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    • 1998
  • Assembly U-lines are increasingly accepted in industry, especially just-in-time production systems, for the efficient utilization of workforce. In this paper, we present an integer programming formulation and a branch-and-bound method for balancing the U-line with the objective of minimizing the number of workstations with a fixed cycle time. In the mathematical model, we provide the method that can reduce the number of variables and constraints. The proposed branch-and-bound method searches the optimal solution based on a depth-first-search. To efficiently search for the optimal solutions to the problems, an assignment rule is used in the method. Bounding strategies and dominance rules are also utilized. Some problems require a large amount of computation time to find the optimal solutions. For this reason. some heuristic fathoming rules are also proposed. Extensive experiments with test-bed problems in the literature are carried out to show the performance of the proposed method. The computational results show that our method is promising in solution quality.

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