• Journal of applied mathematics & informatics
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• v.29 no.5_6
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• pp.1303-1309
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• 2011

Based on the PRP method, a new spectral PRP conjugate gradient method has been proposed to solve general unconstrained optimization problems which produce sufficient descent search direction at every iteration without any line search. Under the Wolfe line search, we prove the global convergence of the new method for general nonconvex functions. The numerical results show that the new method is efficient for the given test problems.

• Journal of the Chungcheong Mathematical Society
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• v.11 no.1
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• pp.129-142
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• 1998

In this paper, we propose a blocked variant of the Conjugate Gradient method which performs as well as and has coarser parallelism than the classical Conjugate Gradient method.

• Journal of the military operations research society of Korea
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• v.24 no.1
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• pp.146-156
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• 1998

Cholesky factorization is known to be inefficient to problems with dense column and network problems in interior point methods. We use the conjugate gradient method and preconditioners to improve the convergence rate of the conjugate gradient method. Several preconditioners were applied to LPABO 5.1 and the results were compared with those of CPLEX 3.0. The conjugate gradient method shows to be more efficient than Cholesky factorization to problems with dense columns and network problems. The incomplete Cholesky factorization preconditioner shows to be the most efficient among the preconditioners.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.740-749
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• 1998

This paper proposes compression of image data using neural networks based on conjugate gradient method and dynamic tunneling system. The conjugate gradient method is applied for high speed optimization .The dynamic tunneling algorithms, which is the deterministic method with tunneling phenomenon, is applied for global optimization. Converging to the local minima by using the conjugate gradient method, the new initial point for escaping the local minima is estimated by dynamic tunneling system. The proposed method has been applied the image data compression of 12 ${\times}$12 pixels. The simulation results shows the proposed networks has better learning performance , in comparison with that using the conventional BP as learning algorithm.

• The Journal of the Acoustical Society of Korea
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• v.20 no.3
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• pp.8-14
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• 2001

In this paper, we present a new cost function for subband block adaptive algorithm and block conjugate gradient algorithm for noise cancellation of acoustic signal. For the cost function, we process the subband signals with data blocks for each subbands and recombine it a whole data block. After these process, the cost function has a quadratic form in adaptive filter coefficients, it guarantees the convergence of the suggested block conjugate gradient algorithm. And the block conjugate gradient algorithm which minimizes the suggested cost function has better performance than the case of full-band block conjugate gradient algorithm, the computer simulation results of noise cancellation show the efficiency of the suggested algorithm.

• Journal of Korean Society of Transportation
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• v.18 no.5
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• pp.99-107
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• 2000

Path-based assignment(PBA) is valuable to dynamic traffic control and routing in integrated ITS framework. As one of widely studied PBA a1gorithms, Gradient Projection(GP) a1gorithm typically fields rapid convergence to a neighborhood of an optimal solution. But once it comes near a solution, it tends to slow down. To overcome this problem, we develop more efficient path-based assignment algorithm by combining Conjugate Gradient method with GP algorithm. It determines more accurate moving direction near a solution in order to gain a significant advantage in speed of convergence. Also this algorithm is applied to the Sioux-Falls network and verified its efficiency. Then we demonstrate that this type of method is very useful in improving speed of convergence in the case of user equilibrium problem.

• Proceedings of the KOR-KST Conference
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• 1999.10a
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• pp.65-70
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• 1999

• Journal of applied mathematics & informatics
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• v.22 no.1_2
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• pp.461-466
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• 2006

In this paper, an algorithm with a new Armijo-type line search is proposed that ensure global convergence of a correlative Polak-Ribiere conjugate method for the unconstrained minimization of non-convex differentiable function.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.22 no.12
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• pp.1715-1725
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• 1998

A two-dimensional transient inverse heat conduction problem involving the estimation of the unknown location, ($X^*$, $Y^*$), and timewise varying unknown strength, $G({\tau})$, of a line heat source embedded inside a rectangular bar with insulated boundaries has been solved simultaneously. The regular conjugate gradient method, RCGM and the modified conjugate gradient method, MCGM with adjoint equation, are used alternately to estimate the unknown strength $G({\tau})$ of the source term, while the parameter estimation approach is used to estimate the unknown location ($X^*$, $Y^*$) of the line heat source. The alternate use of the regular and the modified conjugate gradient methods alleviates the convergence difficulties encountered at the initial and final times (i.e ${\tau}=0$ and ${\tau}={\tau}_f$), hence stabilizes the computation and fastens the convergence of the solution. In order to examine the effectiveness of this approach under severe test conditions, the unknown strength $G({\tau})$ is chosen in the form of rectangular, triangular and sinusoidal functions.

• Journal of applied mathematics & informatics
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• v.34 no.1_2
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• pp.157-165
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• 2016

In this paper, we develop a new hybridization conjugate gradient method for solving the unconstrained optimization problem. Under mild assumptions, we get the sufficient descent property of the given method. The global convergence of the given method is also presented under the Wolfe-type line search and the general Wolfe line search. The numerical results show that the method is also efficient.