• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.81-82
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• 2020

Non-convex 최적화 문제의 복잡도를 완화하도록 해주는 오목함수 결정규칙을 제안한다. 전송용량을 나타내는 유틸리티 함수는 신호와 간섭의 함수이며, non-convex이다. 유틸리티 함수를 간섭관점에서 분석한다. '상대간섭'과 '간섭주요화'를 정의한다. 상대간섭은 D2D 수신단에서의 간섭레벨을 나타낸다. 간섭주요화는 간섭을 주요간섭으로 간략화한다. 간섭주요화를 기반으로 하는 오목함수 결정규칙을 제안한다. 실험결과를 통하여 유틸리티 함수는 상대간섭 0.1 이하에서는 오목함수임을 확인하였다.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.75-83
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• 2020

We consider the D2D utility optimization problem in the cellular system. More specifically, we develop a concave function decision rule which reduces the complexity of non-convex optimization problem. Typically, utility function, which is a function of the signal and the interference, is non-convex. In this paper, we analyze the utility function from the interference perspective. We introduce the 'relative interference' and the 'interference majorization'. The relative interference captures the level of interference at D2D receiver's perspective. The interference majorization approximates the interference by applying the major interference. Accordingly, we propose a concave function decision rule, and the corresponding convex optimization solution. Simulation results show that the utility function is concave when the relative interference is less than 0.1, which is a typical D2D usage scenario. We also show that the proposed convex optimization solution can be applied for such relative interference cases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4835-4855
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• 2018

In this paper, we study the sparse signal recovery that uses information of both support and amplitude of the sparse signal. A convergent iterative algorithm for sparse signal recovery is developed using Majorization-Minimization-based Non-convex Optimization (MM-NcO). Furthermore, it is shown that, typically, the sparse signals that are recovered using the proposed iterative algorithm are not globally optimal and the performance of the iterative algorithm depends on the initial point. Therefore, a modified MM-NcO-based iterative algorithm is developed that uses prior information of both support and amplitude of the sparse signal to enhance recovery performance. Finally, the modified MM-NcO-based iterative algorithm is used to estimate the time-varying sparse wireless channels with temporal correlation. The numerical results show that the new algorithm performs better than related algorithms.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.25 no.3
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• pp.107-116
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• 2021

We introduce optimization algorithms using Bregman Divergence for solving non-negative matrix factorization (NMF) problems. Bregman divergence is known a generalization of some divergences such as Frobenius norm and KL divergence and etc. Some algorithms can be applicable to not only NMF with Frobenius norm but also NMF with more general Bregman divergence. Matrix Factorization is a popular non-convex optimization problem, for which alternating minimization schemes are mostly used. We develop the Bregman proximal gradient method applicable for all NMF formulated in any Bregman divergences. In the derivation of NMF algorithm for Bregman divergence, we need to use majorization/minimization(MM) for a proper auxiliary function. We present algorithmic aspects of NMF for Bregman divergence by using MM of auxiliary function.

• Communications of the Korean Mathematical Society
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• v.10 no.1
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• pp.27-34
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• 1995

For an $n \times n$ matrix $A = [a_{ij}]$, the permanent of A, per A, is defined by $$ per(A) = \sum_{\sigma}{a_{1 \simga(1)} \cdots a_{n \sigma(n)}}, $$ where $\sigma$ runs over all permutations of ${1,\cdots,n}.

• Journal of the Korean Operations Research and Management Science Society
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• v.14 no.2
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• pp.87-96
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• 1989

We develop an algorithm to determine the optimal loading policy, which minimizes the congestion in general queueing networks with variable stations. Under more specified condition, majorization and arrangement ordering are established to compare, respectivity, various loading and assignment policies. Implications of results are also discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.2992-3009
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• 2016

The performance of D-MIMO systems is not only affected by multipath fading but also from shadowing fading, as well as path loss. In this paper, we investigate the ergodic capacity of D-MIMO systems operating in non-correlated K fading (Rayleigh/Gamma) channels. With the aid of majorization and Minkowski theory, we derive analytical closed-form expressions of the upper and lower bounds on the ergodic capacity for D-MIMO systems over non-correlated K fading channels, which are quite general and applicable for arbitrary signal-to-noise ratio (SNR) and the number of transceiver antennas. To intuitively reveal the impacts of system and fading parameters on the ergodic capacity, we deduce asymptotic approximations in the high and low SNR regimes. Finally, we pursue the massive MIMO systems analysis for the lower bound and derive closed-form expressions when the number of antennas at BS grows large, and when the number of antennas at transceivers becomes large with a fixed and finite ratio. It is demonstrated that the proposed expressions on the ergodic capacity accurately match with the theoretical analysis.

• Journal of Korean Institute of Industrial Engineers
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• v.21 no.2
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• pp.289-298
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• 1995

We consider a general queueing network with multi-server stations. The stations are under heavy traffics or moderate variable conditions. We develope an algorithm to determine the optimal loading policy, which minimizes the congestion in a network. Under more specified condition, majorization and arrangement orderings are established to compare, respectively, various loading and assignment policies. Implications of results are also discussed.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.83-84
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• 2020

Sum Utility를 최적화하는 Convex Optimization Algorithm을 제안한다. 일반적으로, Sum Utility 최적화 문제는 Non Convex Optimization Problem이다. 하지만, '상대간섭'과 '간섭주요화'를 활용하여 Non Convex Optimization Problem이 간섭구간에 따라 Convex Optimization으로 해결할 수 있음을 확인하였다. 특히, 유틸리티 함수는 상대간섭 0.1 이하에서는 오목함수임을 확인하였다. 실험결과 상대간섭이 작아질수록 제안하는 알고리즘에 의한 Sum Utility는 증가함을 확인하였다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1987.10a
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• pp.35-35
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• 1987

Consider a job shop that is modelled as an open queueing network of the Jackson(l957) type. All work stations in the shop have the same number of parallel servers. Two problems are studied : the loading of stations and the assignment of servers, which are represented by loading and assingment vectors, respectively. Ma jorization and arrangement orderings are established to order, respectively, the loading and the assignment vectors. It is shown that reducing the loading vector under ma jorizat ion or increasing the assignment vector under arrangement ordering will reduce the congestion in the shop in terms of reducing the total number of jobs(in the sense of likelihood ratio ordering), the maximum queue length(in the sense of stochastic ordering), and the queue-length vector( in the sense of stochastic majorization). The results can be used to supprot production planning in certain job shops, and to aid the desing of storage capacity. (OPEN QUEUEING NETWORK; WJORIZATION; ARRANGEMENT ORDERINC; LIKELIHOOD RATIO ORDERINC; STOCHASTIC ORDERING)