• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.175-178
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• 2000

The object level image compression is a useful technology for reducing the necessary data and manipulating individual objects. In this paper, we propose a new image object compression algorithm that uses the quadratic programming (QP) method to reduce the compressed data. The results indicate the superiority of the proposed QP based algorithm over the low pass extrapolation (LPE) method of MPEG-4.

• Journal of applied mathematics & informatics
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• v.29 no.5_6
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• pp.1395-1407
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• 2011

When a Sequential Quadratic Programming (SQP) method is used to solve the nonlinear programming problems, one of the main difficulties is that the Quadratic Programming (QP) subproblem may be incompatible. In this paper, an SQP algorithm is given by modifying the traditional QP subproblem and applying a class of $l_{\infty}$ penalty function whose penalty parameters can be adjusted automatically. The new QP subproblem is compatible. Under the extended Mangasarian-Fromovitz constraint qualification condition and the boundedness of the iterates, the algorithm is showed to be globally convergent to a KKT point of the non-linear programming problem.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.49 no.2
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• pp.153-160
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• 2013

Control allocation is an important part of a system. It implements the function that map the desired command forces from the controller into the commands of the different actuators. In this paper, the authors present an approach for solving constrained control allocation problem in vessel system by using multi-parametric quadratic programming (mp-QP) algorithm. The goal of mp-QP algorithm applied in this study is to compute a solution to minimize a quadratic performance index subject to linear equality and inequality constraints. The solution can be pre-computed off-line in the explicit form of a piecewise linear (PWL) function of the generalized forces and constrains. The efficiency of mp-QP approach is evaluated through a dynamic positioning simulation for a vessel by using four tugboats with constraints about limited pushing forces and found to work well.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.9
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• pp.908-913
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• 2014

In this paper, we propose a method by which QP (Quadratic Programming) problems can be solved in realtime so that we can implement the predictive control algorithm on a microcontroller system. Firstly, we derive a solution to QP problems by converting the original QP problems to its equivalent least squares with inequalities. Secondly, we propose a predictive control algorithm that can give good realtime computation performance by utilizing the fact that some parameters needed for solving QP problems can be computed offline. Finally, we illustrate that the proposed method can give good realtime features by running the C-code application constructed using the proposed method on a microncontroller system.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.6 s.348
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• pp.23-29
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• 2006

In this paper, we propose an effective global placement technique using quadratic programming(QP). In order to resolve cell congestion problem which is a drawback of QP based placement techniques, additional force and grid pre-warping technique are used. We devised a new density function for evaluating proper additional force which depends on density. Grid pre-warping technique relocates cells over entire area according to the relative ordering between coordinates of cells. Using the additional force obtained by the new density function and applying the pre-warping technique iteratively we obtained a well-distributed global placement. Mongrel which is a middle-down methodology based placer takes such a good global placement as an initial placement and produces a final detailed placement. Experimental results show that proposed technique outperforms the FM algorithm based global placement and are comparable with the well-known leading placers, FengShui, Dragon.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.11 no.4
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• pp.91-102
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• 1991

Traditionally, a dynamic programming(DP) technique has been used to the multireservoir control system. The algorithm has inherent problem to increase computational requirements exponentially due to discretization of variables and expanding the dimension of the system. To solve this problem, this paper describes transforming the optimal control system into a quadratic programming(QP), using Laguerre polynomials(LP) and its properties. The objective function of the proposed QP is independent of time variable. The solution of the QP is obtained by nonlinear programming(NLP) using augmented Lagrangian multiplier method. The numerical experiment shows that the water level of reservoirs is higher than Lee's and the evaluated benefit value is about the same as other researcher's.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.12 no.1
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• pp.59-70
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• 1992

Quadratic programming problems(QP) have been widely used as a direction-finding subproblem in the engineering and structural design optimization. To develop an efficient solution algorithm for the QP subproblems, theoretical aspects and numerical behavior of mathematical programming methods that can be used as QP solver are studied and compared. For the solution of both primal and dual QP, Simplex, gradient projection(GRP), and augmented Lagrange multiplier algorithms are investigated and coded. From the numerical study, it is found that the primal GRP algorithm with potential constraint strategy and the dual Simplex algorithm are more attractive and effective than the others. They have theoretical robustness as well. Moreover, primal GRP algorithm is preferable in case the number of constraints is larger than the number of design variables. Favorable features of GRP and Simplex algorithm are merged into a combined algorithm, which is useful in the structural design optimization.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.839-850
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• 2021

Risk-adjusted return is believed to be one of the optimal parameters to determine an optimum portfolio. A risk-adjusted return is a calculation of the profit or potential profit from an investment that takes into account the degree of risk that must be accepted to achieve it. This paper presents a new procedure in portfolio selection and utilizes these results to optimize the risk level of risk-adjusted Islamic stock portfolios. It deals with the weekly close price of active issuers listed on Jakarta Islamic Index Indonesia for a certain time interval. Overall, this paper highlights portfolio selection, which includes determining the number of stocks, grouping the issuers via technical analysis, and selecting the best risk-adjusted return of portfolios. The nominated portfolio is modeled using Quadratic Programming (QP). The result of this study shows that the portfolio built using the lowest Value at Risk (VaR) outperforms the market proxy on a risk-adjusted basis of M-squared and was chosen as the best portfolio that can be optimized using QP with a minimum risk of 2.86%. The portfolio with the lowest beta, on the other hand, will produce a minimum risk that is nearly 60% lower than the optimal risk-adjusted return portfolio. The results of QP are well verified by a heuristic optimizer of fmincon.

• The Transactions of the Korea Information Processing Society
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• v.7 no.6
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• pp.1867-1873
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• 2000

Function approximation from a set of input-output pairs has numerous applications in scientific and engineering areas. Support vector machine (SVM) is a new and very promising classification, regression and function approximation technique developed by Vapnik and his group at AT&TG Bell Laboratories. However, it has failed to establish itself as common machine learning tool. This is partly due to the fact that this is not easy to implement, and its standard implementation requires the use of optimization package for quadratic programming (QP). In this appear we present simple iterative Kernel Adatron (KA) algorithm for function approximation and compare it with standard SVM algorithm using QP.

• Journal of applied mathematics & informatics
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• v.27 no.3_4
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• pp.567-579
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• 2009

Recently, Peng et al. proposed a primal-dual interior-point method with new search direction and self-regular proximity for LP. This new large-update method has the currently best theoretical performance with polynomial complexity of O($n^{\frac{q+1}{2q}}\;{\log}\;{\frac{n}{\varepsilon}}$). In this paper we use this search direction to propose a primal-dual interior-point method for convex quadratic programming (QP). We overcome the difficulty in analyzing the complexity of the primal-dual interior-point methods for convex quadratic programming, and obtain the same polynomial complexity of O($n^{\frac{q+1}{2q}}\;{\log}\;{\frac{n}{\varepsilon}}$) for convex quadratic programming.