• Title/Summary/Keyword: 2-primal

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POLYNOMIAL CONVERGENCE OF PRIMAL-DUAL ALGORITHMS FOR SDLCP BASED ON THE M-Z FAMILY OF DIRECTIONS

  • Chen, Feixiang
    • Journal of applied mathematics & informatics
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    • v.30 no.1_2
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    • pp.127-133
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    • 2012
  • We establish the polynomial convergence of a new class of path-following methods for SDLCP whose search directions belong to the class of directions introduced by Monteiro [3]. We show that the polynomial iteration-complexity bounds of the well known algorithms for linear programming, namely the short-step path-following algorithm of Kojima et al. and Monteiro and Alder, carry over to the context of SDLCP.

Application of AutoFom III equipment for prediction of primal and commercial cut weight of Korean pig carcasses

  • Choi, Jung Seok;Kwon, Ki Mun;Lee, Young Kyu;Joeng, Jang Uk;Lee, Kyung Ok;Jin, Sang Keun;Choi, Yang Il;Lee, Jae Joon
    • Asian-Australasian Journal of Animal Sciences
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    • v.31 no.10
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    • pp.1670-1676
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    • 2018
  • Objective: This study was conducted to enable on-line prediction of primal and commercial cut weights in Korean slaughter pigs by AutoFom III, which non-invasively scans pig carcasses early after slaughter using ultrasonic sensors. Methods: A total of 162 Landrace, Yorkshire, and Duroc (LYD) pigs and 154 LYD pigs representing the yearly Korean slaughter distribution were included in the calibration and validation dataset, respectively. Partial least squares (PLS) models were developed for prediction of the weight of deboned shoulder blade, shoulder picnic, belly, loin, and ham. In addition, AutoFom III's ability to predict the weight of the commercial cuts of spare rib, jowl, false lean, back rib, diaphragm, and tenderloin was investigated. Each cut was manually prepared by local butchers and then recorded. Results: The cross-validated prediction accuracy ($R^2cv$) of the calibration models for deboned shoulder blade, shoulder picnic, loin, belly, and ham ranged from 0.77 to 0.86. The $R^2cv$ for tenderloin, spare rib, diaphragm, false lean, jowl, and back rib ranged from 0.34 to 0.62. Because the $R^2cv$ of the latter commercial cuts were less than 0.65, AutoFom III was less accurate for the prediction of those cuts. The root mean squares error of cross validation calibration (RMSECV) model was comparable to the root mean squares error of prediction (RMSEP), although the RMSECV was numerically higher than RMSEP for the deboned shoulder blade and belly. Conclusion: AutoFom III predicts the weight of deboned shoulder blade, shoulder picnic, loin, belly, and ham with high accuracy, and is a suitable process analytical tool for sorting pork primals in Korea. However, AutoFom III's prediction of smaller commercial Korean cuts is less accurate, which may be attributed to the lack of anatomical reference points and the lack of a good correlation between the scanned area of the carcass and those traits.

An Efficient Computation of Matrix Triple Products (삼중 행렬 곱셈의 효율적 연산)

  • Im, Eun-Jin
    • Journal of the Korea Society of Computer and Information
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    • v.11 no.3
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    • pp.141-149
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    • 2006
  • In this paper, we introduce an improved algorithm for computing matrix triple product that commonly arises in primal-dual optimization method. In computing $P=AHA^{t}$, we devise a single pass algorithm that exploits the block diagonal structure of the matrix H. This one-phase scheme requires fewer floating point operations and roughly half the memory of the generic two-phase algorithm, where the product is computed in two steps, computing first $Q=HA^{t}$ and then P=AQ. The one-phase scheme achieved speed-up of 2.04 on Intel Itanium II platform over the two-phase scheme. Based on memory latency and modeled cache miss rates, the performance improvement was evaluated through performance modeling. Our research has impact on performance tuning study of complex sparse matrix operations, while most of the previous work focused on performance tuning of basic operations.

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A NEW CLASS OF GENERALIZED CONVEX PROGRAMMING

  • YAN ZHAOXIANG;LI SHIZHENG
    • Journal of applied mathematics & informatics
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    • v.17 no.1_2_3
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    • pp.351-360
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    • 2005
  • This paper finds a new class of generalized convex function which satisfies the following properties: It's level set is $\eta$-convex set; Every feasible Kuhn-Tucker point is a global minimum; If Slater's constraint qualification holds, then every minimum point is Kuhn-Tucker point; Weak duality and strong duality hold between primal problem and it's Mond-Weir dual problem.

ON VARIATIONAL PROBLEMS INVOLVING HIGHER ORDER DERIVATIVES

  • HUSAIN I.;JABEEN Z.
    • Journal of applied mathematics & informatics
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    • v.17 no.1_2_3
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    • pp.433-455
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    • 2005
  • Fritz John, and Karush-Kuhn-Tucker type optimality conditions for a constrained variational problem involving higher order derivatives are obtained. As an application of these Karush-Kuhn-Tucker type optimality conditions, Wolfe and Mond-Weir type duals are formulated, and various duality relationships between the primal problem and each of the duals are established under invexity and generalized invexity. It is also shown that our results can be viewed as dynamic generalizations of those of the mathematical programming already reported in the literature.

Computing Weighted Maximal Flows in Polymatroidal Networks

  • Chung, Nam-Ki
    • Journal of Korean Institute of Industrial Engineers
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    • v.10 no.2
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    • pp.37-43
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    • 1984
  • For the polymatroidal network, which has set-constraints on arcs, solution procedures to get the weighted maximal flows are investigated. These procedures are composed of the transformation of the polymatroidal network flow problem into a polymatroid intersection problem and a polymatroid intersection algorithm. A greedy polymatroid intersection algorithm is presented, and an example problem is solved. The greedy polymatroid intersection algorithm is a variation of Hassin's. According to these procedures, there is no need to convert the primal problem concerned into dual one. This differs from the procedures of Hassin, in which the dual restricted problem is used.

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A GENERALIZATION OF INSERTION-OF-FACTORS-PROPERTY

  • Hwang, Seo-Un;Jeon, Young-Cheol;Park, Kwang-Sug
    • Bulletin of the Korean Mathematical Society
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    • v.44 no.1
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    • pp.87-94
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    • 2007
  • We in this note introduce the concept of g-IFP rings which is a generalization of IFP rings. We show that from any IFP ring there can be constructed a right g-IFP ring but not IFP. We also study the basic properties of right g-IFP rings, constructing suitable examples to the situations raised naturally in the process.

POSTPROCESSING FOR THE RAVIART-THOMAS MIXED FINITE ELEMENT APPROXIMATION OF THE EIGENVALUE PROBLEM

  • Kim, Kwang-Yeon
    • Korean Journal of Mathematics
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    • v.26 no.3
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    • pp.467-481
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    • 2018
  • In this paper we present a postprocessing scheme for the Raviart-Thomas mixed finite element approximation of the second order elliptic eigenvalue problem. This scheme is carried out by solving a primal source problem on a higher order space, and thereby can improve the convergence rate of the eigenfunction and eigenvalue approximations. It is also used to compute a posteriori error estimates which are asymptotically exact for the $L^2$ errors of the eigenfunctions. Some numerical results are provided to confirm the theoretical results.

NONDIFFERENTIABLE SECOND-ORDER MINIMAX MIXED INTEGER SYMMETRIC DUALITY

  • Gulati, Tilak Raj;Gupta, Shiv Kumar
    • Journal of the Korean Mathematical Society
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    • v.48 no.1
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    • pp.13-21
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    • 2011
  • In this paper, a pair of Wolfe type nondifferentiable sec-ond order symmetric minimax mixed integer dual problems is formu-lated. Symmetric and self-duality theorems are established under $\eta_1$-bonvexity/$\eta_2$-boncavity assumptions. Several known results are obtained as special cases. Examples of such primal and dual problems are also given.

CONVERGENCE ANALYSIS OF A NONLINEAR LAGRANGIAN ALGORITHM FOR NONLINEAR PROGRAMMING WITH INEQUALITY CONSTRAINTS

  • Zhang, Li-Wei;Liu, Yong-Jin
    • Journal of applied mathematics & informatics
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    • v.13 no.1_2
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    • pp.1-10
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    • 2003
  • In this paper, we establish a nonlinear Lagrangian algorithm for nonlinear programming problems with inequality constraints. Under some assumptions, it is proved that the sequence of points, generated by solving an unconstrained programming, convergents locally to a Kuhn-Tucker point of the primal nonlinear programming problem.