• Title/Summary/Keyword: Minimax optimization

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Codebook Design Method Based on Minimax Optimization for Data Transmission over WCDMA Voice Channel (WCDMA 음성 채널을 통해 데이터를 전송하기 위한 Minimax 최적화 기반의 코드북 설계 방법)

  • Lee, Junho;Son, Jongmok;Lee, Dong Wook;Park, Yongseok
    • The Journal of the Acoustical Society of Korea
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    • v.34 no.1
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    • pp.82-91
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    • 2015
  • In this paper, a novel codebook design method for data modem over voice channel is presented. Proposed method searches the symbols which have the maximum probability distribution overlap in the symbol space and minimizes the overlap to improve the symbol error rate via minimax optimization. We present numerical simulations and an example implementation. We also give the results of the experiment tests.

MINIMAX PROBLEMS OF UNIFORMLY SAME-ORDER SET-VALUED MAPPINGS

  • Zhang, Yu;Li, Shengjie
    • Bulletin of the Korean Mathematical Society
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    • v.50 no.5
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    • pp.1639-1650
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    • 2013
  • In this paper, a class of set-valued mappings is introduced, which is called uniformly same-order. For this sort of mappings, some minimax problems, in which the minimization and the maximization of set-valued mappings are taken in the sense of vector optimization, are investigated without any hypotheses of convexity.

Global sensitivity analysis improvement of rotor-bearing system based on the Genetic Based Latine Hypercube Sampling (GBLHS) method

  • Fatehi, Mohammad Reza;Ghanbarzadeh, Afshin;Moradi, Shapour;Hajnayeb, Ali
    • Structural Engineering and Mechanics
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    • v.68 no.5
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    • pp.549-561
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    • 2018
  • Sobol method is applied as a powerful variance decomposition technique in the field of global sensitivity analysis (GSA). The paper is devoted to increase convergence speed of the extracted Sobol indices using a new proposed sampling technique called genetic based Latine hypercube sampling (GBLHS). This technique is indeed an improved version of restricted Latine hypercube sampling (LHS) and the optimization algorithm is inspired from genetic algorithm in a new approach. The new approach is based on the optimization of minimax value of LHS arrays using manipulation of array indices as chromosomes in genetic algorithm. The improved Sobol method is implemented to perform factor prioritization and fixing of an uncertain comprehensive high speed rotor-bearing system. The finite element method is employed for rotor-bearing modeling by considering Eshleman-Eubanks assumption and interaction of axial force on the rotor whirling behavior. The performance of the GBLHS technique are compared with the Monte Carlo Simulation (MCS), LHS and Optimized LHS (Minimax. criteria). Comparison of the GBLHS with other techniques demonstrates its capability for increasing convergence speed of the sensitivity indices and improving computational time of the GSA.

Quasi-Deadbeat Minimax Estimation for Deterministic Generic Linear Models

  • Lee, Kwan-Ho;Han, Soo-Hee;Kwon, Wook-Hyun
    • 제어로봇시스템학회:학술대회논문집
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    • 2002.10a
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    • pp.45.5-45
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    • 2002
  • In this paper, a quasi-deadbeat minimax estimation (QME) is proposed as a new class of time-domain parameter estimations for deterministic generic linear models. Linearity, quasi-deadbeat property, FIR structure, and independency of the initial parameter information will be required in advance, in addition to a new performance criterion of a worst case gain between the disturbances and the current estimation error. The proposed QME is obtained in a closed form by directly solving an optimization problem. The QME is represented in both a batch form and an iterative form. A fast algorithm for the suggested estimation is also presented, which is remarkable in view...

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A numerical solver for quantitative pursuit-evasion game (정량적 추적자-회피자 게임을 위한 수치해석기)

  • 이훈구;탁민제
    • 제어로봇시스템학회:학술대회논문집
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    • 1996.10b
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    • pp.474-477
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    • 1996
  • In this paper, a numerical method is developed to solve the 2 dimensional missile/target persuit-evasion game. The numerical solver for the problem is composed of two parts: parametrization of the kinematic equations of motion using collocation and optimization of the parametrized minimax problem using a nonlinear programming. A numerical example is solved to verify the performance of the proposed numerical scheme.

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Two Uncertain Programming Models for Inverse Minimum Spanning Tree Problem

  • Zhang, Xiang;Wang, Qina;Zhou, Jian
    • Industrial Engineering and Management Systems
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    • v.12 no.1
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    • pp.9-15
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    • 2013
  • An inverse minimum spanning tree problem makes the least modification on the edge weights such that a predetermined spanning tree is a minimum spanning tree with respect to the new edge weights. In this paper, the concept of uncertain ${\alpha}$-minimum spanning tree is initiated for minimum spanning tree problem with uncertain edge weights. Using different decision criteria, two uncertain programming models are presented to formulate a specific inverse minimum spanning tree problem with uncertain edge weights involving a sum-type model and a minimax-type model. By means of the operational law of independent uncertain variables, the two uncertain programming models are transformed to their equivalent deterministic models which can be solved by classic optimization methods. Finally, some numerical examples on a traffic network reconstruction problem are put forward to illustrate the effectiveness of the proposed models.

Comparison between Genetic Algorithm and Simplex Method in the Evaluation of Minimum Zone for Flatness (평면도의 최소 영역 평가에서 유전자 알고리듬과 심플렉스 방법의 비교)

  • Hyun, Chang-Hun;Shin, Snag-Choel
    • Journal of Industrial Technology
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    • v.20 no.B
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    • pp.27-34
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    • 2000
  • The definition of flatness is given by ISO, ANSI, KS, etc. but those standards don't mention about the specific methods for the flatness. So various solution models that are based on the Minimum Zone Method have been proposed as an optimization problem for the minimax curve fitting. But it has been rare to compare some optimization algorithms to make a guideline for choosing better algorithms in this field. Hence this paper examined and compared Genetic Algorithm and Simplex Method to the evaluation of flatness. As a result, Genetic Algorithm gave the better or equal flatness than Simplex Method but it has the inefficiency caused from the large number of iteration. Therefore, in the future, another researches about alternative algorithms including Hybrid Genetic Algorithm should be achieved to improve the efficiency of Genetic Algorithm for the evaluation of flatness.

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Robust parameter set selection of unsteady flow model using Pareto optimums and minimax regret approach (파레토 최적화와 최소최대 후회도 방법을 이용한 부정류 계산모형의 안정적인 매개변수 추정)

  • Li, Li;Chung, Eun-Sung;Jun, Kyung Soo
    • Journal of Korea Water Resources Association
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    • v.50 no.3
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    • pp.191-200
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    • 2017
  • A robust parameter set (ROPS) selection framework for an unsteady flow model was developed by combining Pareto optimums obtained by outcomes of model calibration using multi-site observations with the minimax regret approach (MRA). The multi-site calibration problem which is a multi-objective problem was solved by using an aggregation approach which aggregates the weighted criteria related to different sites into one measure, and then performs a large number of individual optimization runs with different weight combinations to obtain Pareto solutions. Roughness parameter structure which can describe the variation of Manning's n with discharges and sub-reaches was proposed and the related coefficients were optimized as model parameters. By applying the MRA which is a decision criterion, the Pareto solutions were ranked based on the obtained regrets related to each Pareto solution, and the top-rated one due to the lowest aggregated regrets of both calibration and validation was determined as the only ROPS. It was found that the determination of variable roughness and the corresponding standardized RMSEs at the two gauging stations varies considerably depending on the combinations of weights on the two sites. This method can provide the robust parameter set for the multi-site calibration problems in hydrologic and hydraulic models.

Parameter estimation of unsteady flow model using mulit-objective optimization and minimax regret approach (다목적최적화와 최소최대 후회도 방법에 의한 부정류 계산모형의 매개변수 추정)

  • Li, Li;Chung, Eun-Sung;Jun, Kyung Soo
    • Proceedings of the Korea Water Resources Association Conference
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    • 2017.05a
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    • pp.310-310
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    • 2017
  • 홍수추적 모형의 적절성을 결정하는 중요한 요소 중 하나는 모형의 매개변수이다. 특히 자연하천에 관한 부정류 계산모형의 매개변수인 조도계수는 하상재료의 특성에 따라 좌우되는 표피마찰뿐만 아니라 하상의 굴곡 등 단면형의 변화에 따른 형상손실 및 하천의 사행에 따른 손실 효과 등을 포괄적으로 내포하고 있기 때문에 모든 하천구간에 대하여 일반적으로 적용할 수 있는 조도계수의 값을 하나로 결정하기는 어렵다. 또한 조도계수는 흐름조건, 즉 유량 또는 수위의 변화에 따른 가변성을 갖고 있기 때문에, 흐름이 시간 및 공간적으로 변화하는 부정류 계산모형에 있어서는 더욱 그러하다. 그러므로 본 연구에서는 조도계수의 가변성과 다수 지점의 관측치를 고려한 모형보정의 결과로부터 얻은 파레토 최적화와 최소최대 후회도 방법(Minimax regret approach, MRA)을 결합하여 부정류 계산모형의 안정적인 매개변수를 선정할 수 있는 방법을 제안하였다. 여러 지점의 관측치를 고려한 모형의 보정은 다목적 최적화 문제로서, 여러 지점에 대한 가중치를 결합하여 얻은 하나의 목적함수에 대하여 여러 번의 개별 최적화를 수행함으로써 다수의 파레토 최적해들을 구할 수 있는 통합접근법을 적용하였다. 이때 유량에 따른 조도계수의 가변성을 나타내는 두 개의 매개변수로 구성된 관계식을 이용하여 두 구간에 대한 매개변수들을 모형의 추정 대상 매개변수로서 최적화하였다. 이 후 각기 다른 홍수사상에 대해 보정과 검증을 수행하였으며 각각에 대한 평가지표의 후회도를 정량화하였고 최종 안정적인 매개변수를 추정하기 위해 MRA를 이용하여 종합적인 순위를 도출하였다. MRA는 완전히 불확실한 의사결정 상황에서 유용한 방법으로 알려져 있는데 가장 나쁜 순위가 가장 좋은 것을 선택할 수 있게 하는 보수적인 의사결정기법이다. 계산결과 추정된 모형의 가변조도계수와 그로부터 얻은 두 개 지점에서의 평가지표인 RMSE는 두 지점에 대한 가중치의 조합에 따라 선택되는 매개변수 값에 따라 달라짐을 알 수 있었다. 본 연구에서 제시한 방법은 수문 및 수리모형의 다수의 관측지점의 자료를 이용한 매개변수 산정문제에 있어서 안정적인 해를 도출할 수 있다.

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