• East Asian mathematical journal
• /
• v.27 no.5
• /
• pp.539-543
• /
• 2011

In this paper, we consider an optimization problem which consists a nonconvex quadratic objective function and two nonconvex quadratic constraint functions. We formulate its dual problem with semidefinite constraints, and we establish weak and strong duality theorems which hold between these two problems. And we give an example to illustrate our duality results. It is worth while noticing that our weak and strong duality theorems hold without convexity assumptions.

• Structural Engineering and Mechanics
• /
• v.2 no.3
• /
• pp.295-309
• /
• 1994

The present paper concerns the mathematical study and the numerical treatment of the problem of semirigid connections in bolted steel column base plates by taking into account the possibility of appearance of separation phenomena on the contact surface under certain loading conditions. In order to obtain a convenient discrete form to simulate the structural behaviour of a steel column base plate, the continuous contact problem is first formulated as a variational inequality problem or, equivalently, as a quadratic programming problem. By applying an appropriate finite element scheme, the discrete problem is formulated as a quadratic optimization problem which expresses, from the standpoint of Mechanics, the principle of minimum potential energy of the semirigid connection at the state of equilibrium. For the numerical treatment of this problem, two effective and easy-to-use solution strategies based on quadratic optimization algorithms are proposed. This technique is illustrated by means of a numerical application.

• Management Science and Financial Engineering
• /
• v.12 no.1
• /
• pp.113-125
• /
• 2006

Bilevel programming problem is a two-stage optimization problem where the constraint region of the first level problem is implicitly determined by another optimization problem. In this paper we consider the bilevel quadratic/linear fractional programming problem in which the objective function of the first level is quasiconcave, the objective function of the second level is linear fractional and the feasible region is a convex polyhedron. Considering the relationship between feasible solutions to the problem and bases of the coefficient submatrix associated to variables of the second level, an enumerative algorithm is proposed which finds a global optimum to the problem.

• Journal of Mechanical Science and Technology
• /
• v.15 no.7
• /
• pp.876-888
• /
• 2001

For a large scaled optimization based on response surface methods, an efficient quadratic approximation method is presented in the context of the trust region model management strategy. If the number of design variables is η, the proposed method requires only 2η+1 design points for one approximation, which are a center point and tow additional axial points within a systematically adjusted trust region. These design points are used to uniquely determine the main effect terms such as the linear and quadratic regression coefficients. A quasi-Newton formula then uses these linear and quadratic coefficients to progressively update the two-factor interaction effect terms as the sequential approximate optimization progresses. In order to show the numerical performance of the proposed method, a typical unconstrained optimization problem and two dynamic response optimization problems with multiple objective are solved. Finally, their optimization results compared with those of the central composite designs (CCD) or the over-determined D-optimality criterion show that the proposed method gives more efficient results than others.

• East Asian mathematical journal
• /
• v.16 no.2
• /
• pp.317-330
• /
• 2000

In this paper we consider the optimal control problem of both operators and parameters for nonlinear hyperbolic systems. For the identification problem, we show that for every value of the parameter and operators, the optimal control problem has a solution. Moreover we obtain the necessary conditions of optimality for the optimal control problem on the system.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.34 no.3
• /
• pp.125-136
• /
• 2009

The container packing problem Is one of the traditional optimization problems, which is very related to the knapsack problem and the bin packing problem. In this paper, we deal with the quadratic multiple container picking problem (QMCPP) and it Is known as a NP-hard problem. Thus, It seems to be natural to use a heuristic approach such as evolutionary algorithms for solving the QMCPP. Until now, only a few researchers have studied on this problem and some evolutionary algorithms have been proposed. This paper introduces a new efficient evolutionary algorithm for the QMCPP. The proposed algorithm is devised by improving the original network random key method, which is employed as an encoding method in evolutionary algorithms. And we also propose local search algorithms and incorporate them with the proposed evolutionary algorithm. Finally we compare the proposed algorithm with the previous algorithms and show the proposed algorithm finds the new best results in most of the benchmark instances.

• East Asian mathematical journal
• /
• v.30 no.2
• /
• pp.149-172
• /
• 2014

The problems of optimization addressed in the high school curriculum are usually posed in real-life contexts. However, because of the instructional purposes, problems are artificially constructed to suit computation, rather than to reflect real-life problems. Those problems have thus limited use for teaching 'practicalities', which is one of the goals of mathematics education. This study, by utilizing 'GeoGebra', suggests the optimization problem solving related to the quadratic curve, using the contour-line method which contemplates the quadratic curve changes successively. By considering more realistic situations to supplement the limit which deals only with numerical and algebraic approach, this attempt will help students to be aware of the usefulness of mathematics, and to develop interests in mathematics, as well as foster students' integrated thinking abilities across units. And this allows students to experience a variety of math.

• Korean Journal of Mathematics
• /
• v.4 no.1
• /
• pp.7-16
• /
• 1996

The optimization problems with quadratic constraints often appear in various fields such as network flows and computer tomography. In this paper, we propose an algorithm for solving those problems and prove the convergence of the proposed algorithm.

• Proceedings of the KSME Conference
• /
• 2000.04b
• /
• pp.144-147
• /
• 2000

A numerical method for the solution of one-dimensional inverse heat conduction problem is established and its performance is demonstrated with computational results. The present work introduces the maximum entropy method in order to build a robust formulation of the inverse problem. The maximum entropy method finds the solution that maximizes the entropy functional under given temperature measurement. The philosophy of the method is to seek the most likely inverse solution. The maximum entropy method converts the inverse problem to a non-linear constrained optimization problem of which constraint is the statistical consistency between the measured temperature and the estimated temperature. The successive quadratic programming facilitates the maximum entropy estimation. The gradient required fur the optimization procedure is provided by solving the adjoint problem. The characteristic feature of the maximum entropy method is discussed with the illustrated results. The presented results show considerable resolution enhancement and bias reduction in comparison with the conventional methods.

• Journal of KIISE:Computer Systems and Theory
• /
• v.36 no.1
• /
• pp.21-25
• /
• 2009

In this paper, we study the problem to fit a piecewise-quadratic polynomial curve to points in the plane. The curve consists of quadratic polynomial segments and two points are connected by a segment. But it passes through a subset of points, and for the points not to be passed, the error between the curve and the points is estimated in $L^{\infty}$ metric. We consider two optimization problems for the above problem. One is to reduce the number of segments of the curve, given the allowed error, and the other is to reduce the error between the curve and the points, while the curve has the number of segments less than or equal to the given integer. For the number n of given points, we propose $O(n^2)$ algorithm for the former problem and $O(n^3)$ algorithm for the latter.