• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.651-662
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• 2017

We propose an alternative procedure to select penalty parameter in $L_1$ penalized robust regression. This procedure is based on marginalization of prior distribution over the penalty parameter. Thus, resulting objective function does not include the penalty parameter due to marginalizing it out. In addition, its estimating algorithm automatically chooses a penalty parameter using the previous estimate of regression coefficients. The proposed approach bypasses cross validation as well as saves computing time. Variable-wise penalization also performs best in prediction and variable selection perspectives. Numerical studies using simulation data demonstrate the performance of our proposals. The proposed methods are applied to Boston housing data. Through simulation study and real data application we demonstrate that our proposals are competitive to or much better than cross-validation in prediction, variable selection, and computing time perspectives.

• Journal of the Korean Mathematical Society
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• v.54 no.2
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• pp.461-477
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• 2017

A dual substructuring method with a penalty term was introduced in the previous works by the authors, which is a variant of the FETI-DP method. The proposed method imposes the continuity not only by using Lagrange multipliers but also by adding a penalty term which consists of a positive penalty parameter ${\eta}$ and a measure of the jump across the interface. Due to the penalty term, the proposed iterative method has a better convergence property than the standard FETI-DP method in the sense that the condition number of the resulting dual problem is bounded by a constant independent of the subdomain size and the mesh size. In this paper, a further study for a dual iterative substructuring method with a penalty term is discussed in terms of its convergence analysis. We provide an improved estimate of the condition number which shows the relationship between the condition number and ${\eta}$ as well as a close spectral connection of the proposed method with the FETI-DP method. As a result, a choice of a moderately small penalty parameter is guaranteed.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.916-921
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• 2004

The penalty method has been widely applied to analyses of incompressible fluid flow. However, we have not yet found any prior studies that employed penalty method to analyze compressible fluid flow. In this study, with an eye on the apparent similarity between the slight compressible formulation and the penalty formulation, we have proposed a new approximate approach that can analyze compressible packing process using the penalty parameter l. Based on the assumption of the isothermal flow, a set of reference solutions was obtained to verify the validity of the proposed scheme. Furthermore, we have applied the proposed scheme to the analysis of the packing process of different cases.

• Journal of Biomedical Engineering Research
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• v.38 no.5
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• pp.219-226
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• 2017

Penalized-likelihood (PL) reconstruction methods for transmission tomography are known to provide improved image quality for reduced dose level by efficiently smoothing out noise while preserving edges. Unfortunately, however, most of the edge-preserving penalty functions used in conventional PL methods contain at least one free parameter which controls the shape of a non-quadratic penalty function to adjust the sensitivity of edge preservation. In this work, to avoid difficulties in finding a proper value of the free parameter involved in a non-quadratic penalty function, we propose a new adaptive method of space-variant smoothing with a simple quadratic penalty function. In this method, the smoothing parameter is adaptively selected for each pixel location at each iteration by using the image roughness measured by a pixel-wise standard deviation image calculated from the previous iteration. The experimental results demonstrate that our new method not only preserves edges, but also suppresses noise well in monotonic regions without requiring additional processes to select free parameters that may otherwise be included in a non-quadratic penalty function.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.105-110
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• 2005

The penalty method has been widely applied to analyses of incompressible fluid flow. However, we have not yet found any prior studies that employed penalty method to analyze compressible fluid flow. In this study, with an eye on the apparent similarity between the slight compressible formulation and the penalty formulation, we have proposed a modified approximate approach that can analyze compressible packing process using the penalty parameter, which is an improvement on an earlier formulation (KSME, 2004B). Based on the assumption of the isothermal flow, a set of reference solutions was obtained to verify the validity of the proposed scheme. Furthermore, we have applied the proposed scheme to the analysis of the packing process of different cases.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.844-847
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• 1997

In this paper, scheduling problem is dealt for the minimization of due date penalty for the customer order. Multiproduct batch processes have been dealt with for their suitability for high value added low volume products. Their scheduling problems take minimization of process operation for objective function, which is not enough to meet the customer satisfaction and the process efficiency simultaneously because of increasing requirement of fast adaptation for rapid changing market condition. So new target function has been suggested by other researches to meet two goals. Penalty function minimization is one of them. To present more precisely production scheduling, we develop new scheduling model with penalty function of earliness and tardiness We can find many real cases that penalty parameters are divergent by the difference between the completion time of operation and due date. That is to say, the penalty parameter values for the product change by the customer demand condition. If the order charges different value for due date, we can solve it with the due date period. The period means the time scope where penalty parameter value is 0. If we make use of the due date period, the optimal sequence of our model is not always same with that of fixed due date point. And if every product have due date period, due date of them are overlapped which needs optimization for the maximum profit and minimum penalty. Due date period extension can be enlarged to makespan minimization if every product has the same abundant due date period and same penalty parameter. We solve this new scheduling model by simulated annealing method. We also develop the program, which can calculate the optimal sequence and display the Gantt chart showing the unit progress and time allocation only with processing data.

• Journal of Mechanical Science and Technology
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• v.14 no.10
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• pp.1122-1130
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• 2000

Based on the value of the Lagrange multiplier and the degree of constraint activeness, a new update rule is proposed for penalty parameters of the ALM method. The theoretical exposition of this suggested update rule is presented by using the algorithmic interpretation and the geometric interpretation of the augmented Lagrangian. This interpretation shows that the penalty parameters can effect the performance of the ALM method. Also, it offers a lower limit on the penalty parameters that makes the augmented Lagrangian to be bounded. This lower limit forms the backbone of the proposed update rule. To investigate the numerical performance of the update rule, it is embedded in our ALM based dynamic response optimizer, and the optimizer is applied to solve six typical dynamic response optimization problems. Our optimization results are compared with those obtained by employing three conventional update rules used in the literature, which shows that the suggested update rule is more efficient and more stable than the conventional ones.

• Proceedings of the Optical Society of Korea Conference
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• 1996.09a
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• pp.96-96
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• 1996

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.673-683
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• 2017

The least absolute shrinkage and selection operator (LASSO) has been a popular regression estimator with simultaneous variable selection. However, LASSO does not have the oracle property and its robust version is needed in the case of heavy-tailed errors or serious outliers. We propose a robust penalized regression estimator which provide a simultaneous variable selection and estimator. It is based on the rank regression and the non-convex penalty function, the smoothly clipped absolute deviation (SCAD) function which has the oracle property. The proposed method combines the robustness of the rank regression and the oracle property of the SCAD penalty. We develop an efficient algorithm to compute the proposed estimator that includes a SCAD estimate based on the local linear approximation and the tuning parameter of the penalty function. Our estimate can be obtained by the least absolute deviation method. We used an optimal tuning parameter based on the Bayesian information criterion and the cross validation method. Numerical simulation shows that the proposed estimator is robust and effective to analyze contaminated data.

• Transactions of the Korean Society of Mechanical Engineers
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• v.14 no.3
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• pp.503-512
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• 1990

In this study, a penalty method effective for the case that has no snap-back phenomenon, is proposed and an adaptive method which choose the penalty method or Riks' type method, is suggested for the case of snap-back problem. And for the case that loads are applied to one or more points of a structure, the Riks' method is applied in general, but under certain condition choice of new incremental load parameter is suggested to accelerate the convergence rate. Finally, for the case that displacements of a structure are controlled at one or more points Riks' type method is proposed. In this case, the proposed method is applied in general but under certain condition it is recommended to choose other incremental displacement parameter to eliminate probable divergence. Five examples are analysed and compared with the result of published literature.