• Journal of Korean Institute of Industrial Engineers
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• v.27 no.4
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• pp.424-431
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• 2001

This paper is concerned with the unconstrained two-dimensional cutting problem of cutting small rectangles (products), each of which has its own profit and size, from a large rectangle (material) to maximize the profit-sum of products. Since this problem is used as a sub-problem to generate a cutting pattern in the algorithms for the two-dimensional cutting stock problem, most of researches for the two-dimensional cutting stock problem have been concentrated on solving this sub-problem more efficiently. This paper improves Hifi and Zissimopoulos's recursive algorithm, which is known as the most efficient exact algorithm, by applying newly proposed upper bound and searching strategy. The experimental results show that the proposed algorithm has been improved significantly in the computational amount of time as compared with the Hifi and Zissimopulos's algorithm.

• Journal of the Korean Society for Railway
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• v.2 no.3
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• pp.9-17
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• 1999

This paper presents a new algorithm to determine the pre-loads that sustain the static equilibrium state in a given position. The algorithm which uses a partial velocity matrix leads to an unconstrained optimization problem to compute the pre-loads of the suspensions. To demonstrate the validity of the proposed algorithm, the static analysis results that employ the pre-loads of three examples are presented using a reliable commercial program. Results of the analysis confirm the validity of the proposed algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.1
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• pp.79-84
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• 2009

In this paper, a best-first branch and bound algorithm based upon the bottom-up approach for the unweighted unconstrained two-dimensional cutting problem is proposed to find the optimal solution to the problem. The algorithm uses simple and effective methods to prevent constructing duplicated patterns and reduces the searching space by dividing the branched node set. It also uses a efficient bounding strategy to fathom the set of patterns. Computational results are compared with veil-known exact algorithms and demonstrate the efficiency of the proposed algorithm.

• Journal of applied mathematics & informatics
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• v.27 no.5_6
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• pp.981-1000
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• 2009

This paper considers asset liability management problems when their deterministic equivalent formulations are general nonlinear optimization problems. The presented approach uses a nonmonotone trust region strategy for solving a sequence of unconstrained subproblems parameterized by a scalar parameter. The objective function of each unconstrained subproblem is an augmented penalty-barrier function that involves both primal and dual variables. Each subproblem is solved approximately. The algorithm does not restrict a monotonic decrease of the objective function value at each iteration. If a trial step is not accepted, the algorithm performs a non monotone line search to find a new acceptable point instead of resolving the subproblem. We prove that the algorithm globally converges to a point satisfying the second-order necessary optimality conditions.

• Journal of the Korean Mathematical Society
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• v.47 no.1
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• pp.63-70
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• 2010

In this paper, we develop a nonmonotone spectral memory gradient method for unconstrained optimization, where the spectral stepsize and a class of memory gradient direction are combined efficiently. The global convergence is obtained by using a nonmonotone line search strategy and the numerical tests are also given to show the efficiency of the proposed algorithm.

• Journal of the Korea Computer Industry Society
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• v.5 no.3
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• pp.377-382
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• 2004

An unconstrained three-dimensional cutting problem describes the process of finding the cutting pattern that yields the maximum total profit-sum for the small parallelepipeds pieces cut from a large parallelepiped box and there is no limit to the number of pieces to be cut. The problem is a classic NP-hard. The bottom-up approach, which generates all of the feasible cutting patterns by combining two other cutting patterns, can be applied to the three-dimensional problem. We introduce a build and new branching strategies for the unconstrained three-dimensional cutting problem. The strategies are all generalized from the branching strategies proposed by G et at. to solve unconstrained two-dimensional cutting problems.

• Journal of KIISE
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• v.41 no.9
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• pp.666-673
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• 2014

This paper proposes a scalable face recognition method for unconstrained face databases, and shows a simple experimental result. Existing face recognition research usually has focused on improving the recognition rate in a constrained environment where illumination, face alignment, facial expression, and background is controlled. Therefore, it cannot be applied in unconstrained face databases. The proposed system is face feature extraction algorithm for unconstrained face recognition. First of all, we extract the area that represent the important features(landmarks) in the face, like the eyes, nose, and mouth. Each landmark is represented by a high-dimensional LBP(Local Binary Pattern) histogram feature vector. The multi-scale LBP histogram vector corresponding to a single landmark, becomes a low-dimensional face feature vector through the feature reduction process, PCA(Principal Component Analysis) and LDA(Linear Discriminant Analysis). We use the Rank acquisition method and Precision at k(p@k) performance verification method for verifying the face recognition performance of the low-dimensional face feature by the proposed algorithm. To generate the experimental results of face recognition we used the FERET, LFW and PubFig83 database. The face recognition system using the proposed algorithm showed a better classification performance over the existing methods.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.327-330
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• 1996

It is well known that the controller output limits have a signifiant effect on the closed loop system performance. Considering the input constraints in GPCF, an effective selection method of the control weighting(.gamma.) is proposed to reduce the amplitude and the rate of control signals so that control signals lie within the limits. It is based on the relation between control weighting(.gamma.) and optimal solution of the unconstrained GPCF. The GPCFIC algorithm chooses an .gamma. at each sampling time so that all unconstrained GPCF output over the control horizon satisfy the rate and the amplitude constraints. In order to evaluate the performance of the GPCFIC, the computer simulations have been done for level control of PWR steam generator in low power operation and shown satisfactory results.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.2
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• pp.141-157
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• 2013

In this paper, we consider the minimization of the total weighted earliness-tardiness penalty of jobs, regarding the partial precedences between jobs. We present an optimal scheduling algorithm in O(n(n+m log m)) where n is the number of jobs and m is the number of partial precedences. In the algorithm, the optimal schedule is constructed iteratively by considering each group of contiguous jobs as a block that is represented by a tree.

• Journal of applied mathematics & informatics
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• v.7 no.3
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• pp.719-738
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• 2000

A dual algorithm based on the smooth function proposed by Polyak (1988) is constructed for solving nonlinear programming problems with inequality constraints. It generates a sequence of points converging locally to a Kuhn-Tucker point by solving an unconstrained minimizer of a smooth potential function with a parameter. We study the relationship between eigenvalues of the Hessian of this smooth potential function and the parameter, which is useful for analyzing the effectiveness of the dual algorithm.