• Journal of applied mathematics & informatics
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• v.6 no.3
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• pp.735-746
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• 1999

Based on an active set strategy a method for solving lin-early constrained indefinite quadratic programs to solve the correspond-ing system of equations at each iteration is presented. The algorithm takes two descent directions to strictly decrease the value of objective function and obtains a suitable step to maintain feasibility. Computa-tional results on a range of quadratic test problems are given.

• Management Science and Financial Engineering
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• v.7 no.2
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• pp.13-30
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• 2001

This paper discusses a paradox in an Indefinite Quadratic transportation Problem. Here, the objective function is the product of two linear functions. A paradox arises when the transportation problem admits of a total cost which is lower than the optimum cost, by transporting larger quantities of goods over the same route. A sufficient condition for the existence of a paradox is established. Paradoxical Range of flow is obtained for any given flow in which the corresponding objective function value is less than the optimum value of the given transportation problem. It is illustrated with the help of a numerical example.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.45.2-45
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• 2002

$\textbullet$ Uncertainties are described by sum quadratic constraint(SQC) $\textbullet$ SQC is converted into an indefinite quadratic cost function $\textbullet$ A Kalman filter developed in indefinite inner product space is Krein space Kalman filter $\textbullet$ To minimize the SQC, the Krein space Kalman filter is used $\textbullet$ The proposed robust filter outperforms the standard Kalman filter and existing robust Kalman filter $\textbullet$ The proposed filter has the same recursive, simple structure as the standard Kalman filter $\textbullet$ Easy to design, adequate for on-line implementation

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.104-109
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• 2002

A new robust Kalman filter is designed for the linear discrete-time system with norm-bounded parametric uncertainties. Sum quadratic constraint, which describes the uncertainties of the system, is converted into an indefinite quadratic form to be minimized in indefinite inner product space. This minimization problem is solved by the new robust Kalman filter. Since the new filter is obtained by simply modifying the conventional Kalman filter, robust filtering scheme can be more readily designed using the proposed method in comparison with the existing robust Kalman filters. A numerical example demonstrates the robustness and the improvement of the proposed filter compared with the existing filters.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.1 no.1
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• pp.91-96
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• 1991

The key exchange idea by using a reduced binary indefinite quadratic form has been introdu-ced. This is based on the difficulty of solving the discrete logarithm problem on the class group of a real quadratic field.

• Journal of applied mathematics & informatics
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• v.15 no.1_2
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• pp.185-200
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• 2004

A stochastic optimal LQR control problem under some integral quadratic (IQ) constraints is studied, with cross terms in both the cost and the constraint functionals, allowing all the control weighting matrices being indefinite. Sufficient conditions for the well-posedness of this problem are given. When these conditions are satisfied, the optimal control is explicitly derived via dual theory.

• Journal of Mechanical Science and Technology
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• v.17 no.3
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• pp.329-335
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• 2003

EALQR (Linear Quadratic Regulate. design with Eigenstructure Assignment capability) has the capability of exact assignment of eigenstructure with the guaranteed margins of the LQR for MIMO (Multi-Input Multi-Output) systems. However, EALQR undergoes a restriction on the state-weighting matrix Q in LQR to be indefinite with respect to the region of allowable closedloop eigenvalues. The definiteness of the weighting matrix is closely related to the robustness property of a given system. In this paper, we derive a relation between the indefinite weighting matrix Q and the robustness property for EALQR. The modified frequency domain inequality, that could be guaranteed by EQLQR with an indefinite weighting matrix, is presented.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.542-547
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• 2003

A new approach to design of a discrete-time robust $H_{\infty}$ filter in finite horizon case is proposed. It is shown that robust $H_{\infty}$ filtering problem can be cast into the minimization problem of an indefinite quadratic form, which can be solved by implementing the Kalman filter defined in Krein space. The proposed filter is readily derived by simply augmenting the state space model and has the robustness property against the parameter uncertainties of a given system.

• Journal of applied mathematics & informatics
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• v.13 no.1_2
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• pp.85-97
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• 2003

An indefinite stochastic linear-quadratic(LQ) optimal control problem with cross term over an infinite time horizon is studied, allowing the weighting matrices to be indefinite. A systematic approach to the problem based on semidefinite programming (SDP) and .elated duality analysis is developed. Several implication relations among the SDP complementary duality, the existence of the solution to the generalized Riccati equation and the optimality of LQ problem are discussed. Based on these relations, a numerical procedure that provides a thorough treatment of the LQ problem via primal-dual SDP is given: it identifies a stabilizing optimal feedback control or determines the problem has no optimal solution. An example is provided to illustrate the results obtained.

• Journal of applied mathematics & informatics
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• v.12 no.1_2
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• pp.155-164
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• 2003

A method is proposed to predict the distances between given residue pairs (between C$\sub$${\alpha}$/ atoms) of a protein using a sequence to structure mapping by indefinite quadratic approximation. The prediction technique requires a data fitting in three dimensional space with coordinates of the residues of known structured proteins and leads to a numerical ref resentation of 20 amino acids by minimizing a large least norm iteratively. These approximations are used in distance prediction for given residue pairs. Some computational experience on a test set of small proteins from Brookhaven Protein Data Bank are given.