• Title/Summary/Keyword: Convex Combination

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SYMMETRIC TOEPLITZ DETERMINANTS ASSOCIATED WITH A LINEAR COMBINATION OF SOME GEOMETRIC EXPRESSIONS

  • Ahuja, Om P.;Khatter, Kanika;Ravichandran, V.
    • Honam Mathematical Journal
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    • v.43 no.3
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    • pp.465-481
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    • 2021
  • Let f be the function defined on the open unit disk, with f(0) = 0 = f'(0) - 1, satisfying Re (αf'(z) + (1 - α)zf'(z)/f(z)) > 0 or Re (αf'(z) + (1 - α)(1 + zf"(z)/f'(z)) > 0 respectively, where 0 ≤ α ≤ 1. Estimates for the Toeplitz determinants have been obtained when the elements are the coefficients of the functions belonging to these two subclasses.

THE ZEROS OF CERTAIN FAMILY OF SELF-RECIPROCAL POLYNOMIALS

  • Kim, Seon-Hong
    • Bulletin of the Korean Mathematical Society
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    • v.44 no.3
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    • pp.461-473
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    • 2007
  • For integral self-reciprocal polynomials P(z) and Q(z) with all zeros lying on the unit circle, does there exist integral self-reciprocal polynomial $G_r(z)$ depending on r such that for any r, $0{\leq}r{\leq}1$, all zeros of $G_r(z)$ lie on the unit circle and $G_0(z)$ = P(z), $G_1(z)$ = Q(z)? We study this question by providing examples. An example answers some interesting questions. Another example relates to the study of convex combination of two polynomials. From this example, we deduce the study of the sum of certain two products of finite geometric series.

Generalized Cross Decomposition Algorithm for Large Scale Optimization Problems with Applications (대규모 최적화 문제의 일반화된 교차 분할 알고리듬과 응용)

  • Choi, Gyung-Hyun;Kwak, Ho-Mahn
    • Journal of Korean Institute of Industrial Engineers
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    • v.26 no.2
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    • pp.117-127
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    • 2000
  • In this paper, we propose a new convex combination weight rule for the cross decomposition method which is known to be one of the most reliable and promising strategies for the large scale optimization problems. It is called generalized cross decomposition, a modification of linear mean value cross decomposition for specially structured linear programming problems. This scheme puts more weights on the recent subproblem solutions other than the average. With this strategy, we are having more room for selecting convex combination weights depending on the problem structure and the convergence behavior, and then, we may choose a rule for either faster convergence for getting quick bounds or more accurate solution. Also, we can improve the slow end-tail behavior by using some combined rules. Also, we provide some computational test results that show the superiority of this strategy to the mean value cross decomposition in computational time and the quality of bounds.

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An Additive Sparse Penalty for Variable Selection in High-Dimensional Linear Regression Model

  • Lee, Sangin
    • Communications for Statistical Applications and Methods
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    • v.22 no.2
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    • pp.147-157
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    • 2015
  • We consider a sparse high-dimensional linear regression model. Penalized methods using LASSO or non-convex penalties have been widely used for variable selection and estimation in high-dimensional regression models. In penalized regression, the selection and prediction performances depend on which penalty function is used. For example, it is known that LASSO has a good prediction performance but tends to select more variables than necessary. In this paper, we propose an additive sparse penalty for variable selection using a combination of LASSO and minimax concave penalties (MCP). The proposed penalty is designed for good properties of both LASSO and MCP.We develop an efficient algorithm to compute the proposed estimator by combining a concave convex procedure and coordinate descent algorithm. Numerical studies show that the proposed method has better selection and prediction performances compared to other penalized methods.

CONVEX POLYTOPES OF GENERALIZED DOUBLY STOCHASTIC MATRICES

  • Cho, Soo-Jin;Nam, Yun-Sun
    • Communications of the Korean Mathematical Society
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    • v.16 no.4
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    • pp.679-690
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    • 2001
  • Doubly stochastic matrices are n$\times$n nonnegative ma-trices whose row and column sums are all 1. Convex polytope $\Omega$$_{n}$ of doubly stochastic matrices and more generally (R,S), so called transportation polytopes, are important since they form the domains for the transportation problems. A theorem by Birkhoff classifies the extremal matrices of , $\Omega$$_{n}$ and extremal matrices of transporta-tion polytopes (R,S) were all classified combinatorially. In this article, we consider signed version of $\Omega$$_{n}$ and (R.S), obtain signed Birkhoff theorem; we define a new class of convex polytopes (R,S), calculate their dimensions, and classify their extremal matrices, Moreover, we suggest an algorithm to express a matrix in (R,S) as a convex combination of txtremal matrices. We also give an example that a polytope of signed matrices is used as a domain for a decision problem. In this context of finite reflection(Coxeter) group theory, our generalization may also be considered as a generalization from type $A_{*}$ n/ to type B$_{n}$ D$_{n}$. n/.

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Decomposition based on Object of Convex Shapes Using Poisson Equation (포아송 방정식을 이용한 컨벡스 모양의 형태 기반 분할)

  • Kim, Seon-Jong;Kim, Joo-Man
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.14 no.5
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    • pp.137-144
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    • 2014
  • This paper proposes a novel procedure that uses a combination of overlapped basic convex shapes to decompose 2D silhouette image. A basic convex shape is used here as a structuring element to give a meaningful interpretation to 2D images. Poisson equation is utilized to obtain the basic shapes for either the whole image or a partial region or segment of an image. The reconstruction procedure is used to combine the basic convex shapes to generate the original shape. The decomposition process involves a merging stage, filtering stage and finalized by compromising stage. The merging procedure is based on solving Poisson's equation for two regions satisfying the same symmetrical conditions which leads to finding equivalencies between basic shapes that need to be merged. We implemented and tested our novel algorithm using 2D silhouette images. The test results showed that the proposed algorithm lead to an efficient shape decomposition procedure that transforms any shape into a simpler basic convex shapes.

Extraction of Attentive Objects Using Feature Maps (특징 지도를 이용한 중요 객체 추출)

  • Park Ki-Tae;Kim Jong-Hyeok;Moon Young-Shik
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.43 no.5 s.311
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    • pp.12-21
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    • 2006
  • In this paper, we propose a technique for extracting attentive objects in images using feature maps, regardless of the complexity of images and the position of objects. The proposed method uses feature maps with edge and color information in order to extract attentive objects. We also propose a reference map which is created by integrating feature maps. In order to create a reference map, feature maps which represent visually attentive regions in images are constructed. Three feature maps including edge map, CbCr map and H map are utilized. These maps contain the information about boundary regions by the difference of intensity or colors. Then the combination map which represents the meaningful boundary is created by integrating the reference map and feature maps. Since the combination map simply represents the boundary of objects we extract the candidate object regions including meaningful boundaries from the combination map. In order to extract candidate object regions, we use the convex hull algorithm. By applying a segmentation algorithm to the area of candidate regions to separate object regions and background regions, real object regions are extracted from the candidate object regions. Experiment results show that the proposed method extracts the attentive regions and attentive objects efficiently, with 84.3% Precision rate and 81.3% recall rate.

DIRECTIONAL CONVEXITY OF COMBINATIONS OF HARMONIC HALF-PLANE AND STRIP MAPPINGS

  • Beig, Subzar;Ravichandran, Vaithiyanathan
    • Communications of the Korean Mathematical Society
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    • v.37 no.1
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    • pp.125-136
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    • 2022
  • For k = 1, 2, let $f_k=h_k+{\bar{g_k}}$ be normalized harmonic right half-plane or vertical strip mappings. We consider the convex combination ${\hat{f}}={\eta}f_1+(1-{\eta})f_2={\eta}h_1+(1-{\eta})h_2+{\overline{\bar{\eta}g_1+(1-\bar{\eta})g_2}}$ and the combination ${\tilde{f}}={\eta}h_1+(1-{\eta})h_2+{\overline{{\eta}g_1+(1-{\eta})g_2}}$. For real 𝜂, the two mappings ${\hat{f}}$ and ${\tilde{f}}$ are the same. We investigate the univalence and directional convexity of ${\hat{f}}$ and ${\tilde{f}}$ for 𝜂 ∈ ℂ. Some sufficient conditions are found for convexity of the combination ${\tilde{f}}$.

Multiresolution Mesh Editing based on the Extended Convex Combination Parameterization (확장 볼록 조합 매개변수화 기반의 다중해상도 메쉬 편집)

  • 신복숙;김형석;김하진
    • Journal of Korea Multimedia Society
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    • v.6 no.7
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    • pp.1302-1311
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    • 2003
  • This paper presents a more stable method of multiresolution editing for a triangular mesh. The basic idea of our paper is to embed an editing area of a mesh onto a 2D region and to produce 3D surfaces which interpolate the editing-information. In this paper, we adopt the extended convex combination approach based on the shape-preserving parameterization for the embedding, which guarantees no self-intersection on the 2D embedded mesh. That is, the result of the embedding is stable. Moreover, we adopt the multi-level B-spline approach to generate the surface containing all of 3D editing-information, which can make us control the editing area in several levels. Hence, this method supports interactive editing and thus can produce intuitive editing results.

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Construction of Modified Yield Loci with Respect to the Strain Rates using Hill48 Quadratic Yield Function (Hill48 이차 항복식을 이용한 변형률 속도에 따른 수정된 항복곡면의 구성)

  • Lee, Chang-Soo;Bae, Gi-Hyun;Kim, Seok-Bong;Huh, Hoon
    • Transactions of the Korean Society of Automotive Engineers
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    • v.18 no.2
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    • pp.56-60
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    • 2010
  • Since the forming process involves the strain rate effect, a yield function considering the strain rate is indispensible to predict the accurate final blank shape in the forming simulation. One of the most widely used in the forming analysis is the Hill48 quadratic yield function due to its simplicity and low computing cost. In this paper, static and dynamic uni-axial tensile tests according to the loading direction have been carried out in order to measure the yield stress and the r-value. Based on the measured results, the Hill48 yield loci have been constructed, and their performance to describe the plastic anisotropy has been quantitatively evaluated. The Hill48 quadratic yield function has been modified using convex combination in order to achieve accurate approximation of anisotropy at the rolling and transverse direction.