• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4355-4371
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• 2020

Phase retrieval, recovering a signal from phaseless measurements, is generally considered to be an NP-hard problem. This paper adopts an amplitude-based nonconvex optimization cost function to develop a new stochastic gradient algorithm, named stochastic reweighted phase retrieval (SRPR). SRPR is a stochastic gradient iteration algorithm, which runs in two stages: First, we use a truncated sample stochastic variance reduction algorithm to initialize the objective function. The second stage is the gradient refinement stage, which uses continuous updating of the amplitude-based stochastic weighted gradient algorithm to improve the initial estimate. Because of the stochastic method, each iteration of the two stages of SRPR involves only one equation. Therefore, SRPR is simple, scalable, and fast. Compared with the state-of-the-art phase retrieval algorithm, simulation results show that SRPR has a faster convergence speed and fewer magnitude-only measurements required to reconstruct the signal, under the real- or complex- cases.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.240-242
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• 1988

This paper proposes an improved ART (Algebraic Reconstruction technique) algorithm. This algorithm is an iterative one with iteration sequence criteria based on the discrepancy between measurement and pseudo-projection data. The simulation result using the proposed algorithm shows a significant improvement in convergency rate over the conventional ART algorithm.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.3
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• pp.33-42
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• 2003

This paper describes an efficient design of an asynchronous 16-bit divider using the NST (new Svoboda-Tung) algorithm. The divider is designed to reduce power consumption by using the asynchronous design scheme in which the division operation is performed only when it is requested. The divider consists of three blocks, i.e. pre-scale block, iteration step block, and on-the-fly converter block using asynchronous pipeline structure. The pre-scale block is designed using a new subtracter to have small area and high performance. The iteration step block consists of an asynchronous ring structure with 4 division steps for area reduction. In other to reduce hardware overhead, the part related to critical path is designed by a dual-rail circuit, and the other part is done by a single-rail circuit in the ring structure. The on-the-fly converter block is designed for high performance using the on-the-fly algorithm that enables parallel operation with iteration step block. The design results with 0.6${\mu}{\textrm}{m}$ CMOS process show that the divider consists of 12,956 transistors with 1,480 $\times$1,200${\mu}{\textrm}{m}$$^2$area and average-case delay is 41.7㎱.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.176.4-176
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• 2001

In this paper, a novel type of iterative learning controller is studied. The proposed learning algorithm utilizes not only the error signal of the previous iteration but also the delayed error signal of the current iteration. The delayed error signal is adopted to improve the convergence speed. The convergence condition is examined and the result shows that the proposed learning algorithm shows the fast convergence speed under the same convergence condition of the traditional iterative learning algorithm. The simulation examples are presented to confirm the validity of the proposed ILC algorithm.

• Proceedings of the Korea Society for Simulation Conference
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• 1998.03a
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• pp.11-16
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• 1998

The classical algorithm for solving liner network flow problems are primal cost improvement method, including simplex method, which iteratively improve the primal cost by moving flow around simple cycles, which iteratively improve the dual cost by changing the prices of a subset of nodes by equal amounts. Typical iteration/shortest path algorithm is used to improve flow problem of liner network structure. In this paper we stdudied about the implemental method of shortest path which is a practical computational aspects. This method can minimize the best neighbor node and also implement the typical iteration which is $\varepsilon$-CS satisfaction using the auction algorithm of linear network flow problem

• Journal of applied mathematics & informatics
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• v.38 no.3_4
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• pp.277-290
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• 2020

We propose and analyze a chord summation algorithm, which combines the ideas of Viète and Archimedes to calculate the value of π. The error of the algorithm decreases exponentially per iteration and becomes pinched at a critical iteration, depending on the accuracy of the first input value, ${\sqrt{2}}$. The critical iteration is also analyzed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4864-4882
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• 2016

In this work, we consider the optimization problem of minimizing energy consumption for real-time multicast over wireless multi-hop networks. Previously, a distributed primal-dual subgradient algorithm was used for finding a solution to the optimization problem. However, the traditional subgradient algorithms have drawbacks in terms of i) sensitivity to iteration parameters; ii) need for saving previous iteration results for computing the optimization results at the current iteration. To overcome these drawbacks, using a joint network coding and scheduling optimization framework, we propose a novel distributed primal-dual Random Deflected Subgradient (RDS) algorithm for solving the optimization problem. Furthermore, we derive the corresponding recursive formulas for the proposed RDS algorithm, which are useful for practical applications. In comparison with the traditional subgradient algorithms, the illustrated performance results show that the proposed RDS algorithm can achieve an improved optimal solution. Moreover, the proposed algorithm is stable and robust against the choice of parameter values used in the algorithm.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.11
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• pp.1852-1861
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• 1997

An enhanced subspace iteration algorithm has been developed to solve eigenvalue problems reliably and efficiently. Basic subspace iteration algorithm has been improved by eliminating recalculation of converged eigenvectors, using Krylov sequence as initial vectors and incorporating with shifting techniques. The number of iterations and computational time have been considerably reduced when compared with the original one, and reliability for catching copies of the multiple roots has been retained successfully. Further research would be required for mathematical justification of the present method.

• Nonlinear Functional Analysis and Applications
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• v.26 no.5
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• pp.995-1009
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• 2021

Many iterative algorithms like that Picard, Mann, Ishikawa and S-iteration are very useful to elucidate the fixed point problems of a nonlinear operators in various topological spaces. The recent trend for elucidate the fixed point via inertial iterative algorithm, in which next iterative depends on more than one previous terms. The purpose of the paper is to establish convergence theorems of new inertial Picard normal S-iteration algorithm for nonexpansive mapping in Hilbert spaces. The comparison of convergence of InerNSP and InerPNSP is done with InerSP (introduced by Phon-on et al. [25]) and MSP (introduced by Suparatulatorn et al. [27]) via numerical example.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.4
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• pp.713-718
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• 1987

In this paper, an iterative restoration algorithm from power spectral density with 1 bit sign information of real part of two dimensional Fourier transform of image corrupted by additive white Gaussian noise is proposed. This method is a modified version of image reconstruction algorithm from power spectral density. From the results of computer simulation with original 32 gray level imgae of 64x64 pixels, we can find that restorated image after each iteration converge to original image very fast, and SNR gain be at least 8[dB] after 10th iteration for corrupted image with additive white Gaussian noise.