• 제목/요약/키워드: minimax theory

검색결과 15건 처리시간 0.019초

A NON-COMPACT GENERALIZATION OF HORVATH'S INTERSECTION THEOREM$^*$

  • Kim, Won-Kyu
    • 대한수학회보
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    • 제32권2호
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    • pp.153-162
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    • 1995
  • Ky Fan's minimax inequality is an important tool in nonlinear functional analysis and its applications, e.g. game theory and economic theory. Since Fan gave his minimax inequality in [2], various extensions of this interesting result have been obtained (see [4,11] and the references therein). Using Fan's minimax inequality, Ha [6] obtained a non-compact version of Sion's minimax theorem in topological vector spaces, and next Geraghty-Lin [3], Granas-Liu [4], Shih-Tan [11], Simons [12], Lin-Quan [10], Park-Bae-Kang [17], Bae-Kim-Tan [1] further generalize Fan's minimax theorem in more general settings. In [9], using the concept of submaximum, Komiya proved a topological minimax theorem which also generalized Sion's minimax theorem and another minimax theorem of Ha in [5] without using linear structures. And next Lin-Quan [10] further generalizes his result to two function versions and non-compact topological settings.

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On the Minimax Disparity Obtaining OWA Operator Weights

  • Hong, Dug-Hun
    • 한국지능시스템학회논문지
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    • 제19권2호
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    • pp.273-278
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    • 2009
  • The determination of the associated weights in the theory of ordered weighted averaging (OWA) operators is one of the important issue. Recently, Wang and Parkan [Information Sciences 175 (2005) 20-29] proposed a minimax disparity approach for obtaining OWA operator weights and the approach is based on the solution of a linear program (LP) model for a given degree of orness. Recently, Liu [International Journal of Approximate Reasoning, accepted] showed that the minimum variance OWA problem of Fuller and Majlender [Fuzzy Sets and Systems 136 (2003) 203-215] and the minimax disparity OWA problem of Wang and Parkan always produce the same weight vector using the dual theory of linear programming. In this paper, we give an improved proof of the minimax disparity problem of Wang and Parkan while Liu's method is rather complicated. Our method gives the exact optimum solution of OWA operator weights for all levels of orness, $0\leq\alpha\leq1$, whose values are piecewise linear and continuous functions of $\alpha$.

ON THE MINIMAX VARIANCE ESTIMATORS OF SCALE IN TIME TO FAILURE MODELS

  • Lee, Jae-Won;Shevlyakov, Georgy-L.
    • 대한수학회보
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    • 제39권1호
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    • pp.23-31
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    • 2002
  • A scale parameter is the principal parameter to be estimated, since it corresponds to one of the main reliability characteristics, namely the average time to failure. To provide robustness of scale estimators to gross errors in the data, we apply the Huber minimax approach in time to failure models of the statistical reliability theory. The minimax valiance estimator of scale is obtained in the important particular case of the exponential distribution.

ELEMENTS OF THE KKM THEORY FOR GENERALIZED CONVEX SPACE

  • Park, Se-Hei
    • Journal of applied mathematics & informatics
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    • 제7권1호
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    • pp.1-28
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    • 2000
  • In the present paper, we introduce fundamental results in the KKM theory for G-convex spaces which are equivalent to the Brouwer theorem, the Sperner lemma, and the KKM theorem. Those results are all abstract versions of known corresponding ones for convex subsets of topological vector spaces. Some earlier applications of those results are indicated. Finally, We give a new proof of the Himmelberg fixed point theorem and G-convex space versions of the von Neumann type minimax theorem and the Nash equilibrium theorem as typical examples of applications of our theory.

A DUAL ALGORITHM FOR MINIMAX PROBLEMS

  • HE SUXIANG
    • Journal of applied mathematics & informatics
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    • 제17권1_2_3호
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    • pp.401-418
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    • 2005
  • In this paper, a dual algorithm, based on a smoothing function of Bertsekas (1982), is established for solving unconstrained minimax problems. It is proven that a sequence of points, generated by solving a sequence of unconstrained minimizers of the smoothing function with changing parameter t, converges with Q-superlinear rate to a Kuhn-Thcker point locally under some mild conditions. The relationship between the condition number of the Hessian matrix of the smoothing function and the parameter is studied, which also validates the convergence theory. Finally the numerical results are reported to show the effectiveness of this algorithm.

복합실험기준의 설정: 모형과 분산구조 (Composite Design Criteria : Model and Variance)

  • 김영일
    • 응용통계연구
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    • 제13권2호
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    • pp.393-405
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    • 2000
  • 원래 최적실험의 이론은 주어진 모형과 그에 따른 가정에 기초하여 발달되었기 때문에 하나의 최적실험기준이 실험이 가족 있는 여러 목적을 모두 반영하는 것이 무리이다. 따라서 실험자가 다목적 실험기준의 필요성을 느끼는 경우에는 종종 여러 최적실험 기준들의 균형을 이루는 방법을 통해 이러한 문제가 다루어진다. 본 연구에서는 이 분산 구조를 가지고 있는 모형을 예를 들어 복합적인 실험기준들을 알아본다. 왜냐하면 이분산인 경우 D-최적과 G-최적실험간의 동격이론은 더 이상 성립되지 않음에 따라 두 실험기준의 특징은 현격하게 구분되어지기 때문이다. 제약조건최적실험, 결합최적실험, 그리고 minimax 설험방법을 통한 실험기준들간의 균형을 꾀하여 보았다. 처음 두 방법은 실험자의 주관이 반영되어 실제적으로 매우 세심한 주의가 필요한 반면, minimax는 그러한 점을 해소하였다고 본다. 또한 이를 확장하여 오차의 이분산 구조에 대한 불확실성이 존재할 때 적용될수 있는 두 가지 실험기준도 마련하여 보았다. 간단한 알고리즘과 결어를 첨부하였다.

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