• Current Optics and Photonics
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• v.5 no.3
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• pp.322-328
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• 2021

In this paper, a pretreatment method for a matrix in convex optimization is proposed to optimize the spectral reconstruction process of a disordered dispersion spectrometer. Unlike the reconstruction process of traditional spectrometers using Fourier transforms, the reconstruction process of disordered dispersion spectrometers involves solving a large-scale matrix equation. However, since the matrices in the matrix equation are obtained through measurement, they contain uncertainties due to out of band signals, background noise, rounding errors, temperature variations and so on. It is difficult to solve such a matrix equation by using ordinary nonstationary iterative methods, owing to instability problems. Although the smoothing Tikhonov regularization approach has the ability to approximatively solve the matrix equation and reconstruct most simple spectral shapes, it still suffers the limitations of reconstructing complex and irregular spectral shapes that are commonly used to distinguish different elements of detected targets with mixed substances by characteristic spectral peaks. Therefore, we propose a special pretreatment method for a matrix in convex optimization, which has been proved to be useful for reducing the condition number of matrices in the equation. In comparison with the reconstructed spectra gotten by the previous ordinary iterative method, the spectra obtained by the pretreatment method show obvious accuracy.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.870-875
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• 2004

Beam stiffeners have frequently been used for raising natural frequencies of base structures. In stiffener layout optimization problems, most of the previous researches considering the position and/or the length of the stiffener as design variables dealt with structures having just simple convex shapes such as a square or rectangle. The reason is concave shape structures have difficulties ill formulating geometry constraints. In this paper, a new geometry constraint handling technique, which can define both convex and concave feasible lesions and measure a degree of geometry constraint violation, is proposed. Evolution strategies (ESs) is utilized as an optimization tool. In addition, the constraint-handling technique of EVOSLINOC (EVOlution Strategy for scalar optimization with Lineal and Nonlinear Constraints) is utilized to solve constrained optimization problems. From a numerical example, the proposed geometry constraint handling technique is verified and proves that the technique can easily be applied to structures in net only convex but also concave shapes, even with a protrusion or interior holes.

• Journal of the Chungcheong Mathematical Society
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• v.4 no.1
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• pp.55-59
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• 1991

We consider perturbation problems and Lagrangians for convex set function optimization problems. In particular, we prove that the Lagrangian $L({\Omega},y)$ is a convex set function in ${\Omega}$ for each y if the perturbation function is convex.

• Journal of applied mathematics & informatics
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• v.16 no.1_2
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• pp.91-104
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• 2004

A modification of the gradient method of convex programming is introduced. Also, we describe symbolic implementation of the gradient method and its modification by means of the programming language MATHEMATICA. A few numerical examples are reported.

• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.1
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• pp.1-17
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• 1996

There have been considerable researches for solving large-scale separable convex optimization ptoblems. In this paper we present a method for large-scale nonseparable smooth convex optimization problems with block-angular linear constraints. One of them is occurred in reconfiguration of the virtual path network which finds the routing path and assigns the bandwidth of the path for each traffic class in ATM (Asynchronous Transfer Mode) network [1]. The solution is approximated by solving a sequence of the block-angular structured separable quadratic programming problems. Bundle-based decomposition method [10, 11, 12]is applied to each large-scale separable quadratic programming problem. We implement the method and present some computational experiences.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1636-1648
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• 2006

In this paper, we propose a new optimization approach to the rate control problem in a wired-cum-wireless network. A primal-dual interior-point(PDIP) algorithm is used to find the solution of the rate optimization problem. We show a comparison between the dual-based(DB) algorithm and PDIP algorithm for solving the rate control problem in the wired-cum-wireless network. The PDIP algorithm performs much better than the DB algorithm. The PDIP can be considered as an attractive method to solve the rate control problem in network. We also present a numerical example and simulation to illustrate our conclusions.

• Journal of applied mathematics & informatics
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• v.22 no.1_2
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• pp.193-208
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• 2006

A modified BFGS algorithm for solving the unconstrained optimization, whose Hessian matrix at the minimum point of the convex function is of rank defects, is presented in this paper. The main idea of the algorithm is first to add a modified term to the convex function for obtain an equivalent model, then simply the model to get the modified BFGS algorithm. The superlinear convergence property of the algorithm is proved in this paper. To compared with the Tensor algorithms presented by R. B. Schnabel (seing [4],[5]), this method is more efficient for solving singular unconstrained optimization in computing amount and complication.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5328-5346
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• 2016

An optimal power control algorithm based on convex optimization is proposed for base stations in long term evolution networks. An objective function was formulated to maximize the proportional fairness of the networks. The optimal value of the objective function was obtained using convex optimization and distributed methods based on the path loss model between the base station and users. Field tests on live networks were conducted to evaluate the performance of the proposed algorithm. The experimental results verified that, in a multi-cell multi-user scenario, the proposed algorithm increases system throughputs, proportional fairness, and energy efficiency by 9, 1.31 and 20.2 %, respectively, compared to the conventional fixed power allocation method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.476-479
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• 2000

This paper deals with a robust pole placement method for uncertain linear systems. For all admissible uncertain parameters, a static output feedback controller is designed such that all the poles of the closed loop system are located within the prespecfied disk. It is shown that the existence of a positive definite matrix belonging to a convex set such that its inverse belongs to another convex set guarantees the existence of the output feedback gain matrix for our control problem. By a sequence of convex optimization the aforementioned matrix is obtained. A numerical example is solved in order to illustrate efficacy of our design method.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.2
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• pp.99-107
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• 2007

We consider (worst-case) robust optimization versions of the Economic Order Quantity (EOQ) model with decreasing cost functions. Two variants of the EOQ model are discussed, in which the purchasing costs are decreasing power functions in either the order quantity or demand rate. We develop the corresponding worst-case robust optimization models of the two variants, where the parameters in the purchasing cost function of each model are uncertain but known to lie in an ellipsoid. For the robust EOQ model with the purchasing cost being a decreasing function of the demand rate, we derive the analytical optimal solution. For the robust EOQ model with the purchasing cost being a decreasing function of the order quantity, we prove that it is a convex optimization problem, and thus lends itself to efficient numerical algorithms.