• Title/Summary/Keyword: Dynamic programming

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An Efficient Method for Multiple Sequence Alignment using Subalignment Refinement (부분서열정렬 개선 기법을 사용한 효율적인 복수서열정렬에 관한 알고리즘)

  • Kim, Jin;Jung, Woo-Cheol;Uhmn, Saang-Yong
    • Journal of KIISE:Software and Applications
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    • v.30 no.9
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    • pp.803-811
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    • 2003
  • Multiple sequence alignment is a useful tool to identify the relationships among protein sequences. Dynamic programming is the most widely used algorithm to obtain multiple sequence alignment with optimal cost. However, dynamic programming cannot be applied to certain cost function due to its drawback and cannot be used to produce optimal multiple sequence alignment. We propose sub-alignment refinement algorithm to overcome the problem of dynamic programming. Also we show proposed algorithm can solve the problem of dynamic programming efficiently.

An effcient algorithm for multiple sequence alignment (복수 염기서열 정렬을 위한 한 유용성 알고리즘)

  • Kim, Jin;Song, Min-Dong
    • Proceedings of the Korean Information Science Society Conference
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    • 1998.10c
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    • pp.51-53
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    • 1998
  • 3개 이상의 DNA 혹은 단백질의 염기서열을 정렬하는 복수 염기서열 정렬(multiple sequence alignment)방법은 염기서열들 사이의 진화관계, gene regulation, 단백질의 구조와 기능에 관한 연구에 필수적인 도구이다. 복수 염기서열 정렬문제는 NP-complete 문제군에 속하며, 이 문제를 해결하기 위하여 가장 유용하게 사용되는 알고리즘으로는 dynamic programming이 있다. Dynamic programming은 주어진 입력 염기서열 군들에 대한 최적의 정렬을 생산할 수 있다. 그러나 dynamic programming의 단점은 오랜 실행시간이 요구되며, 때로는 dynamic programming의 속성 때문에 이 알고리즘을 사용하여도 주어진 입력 염기서열 군들에 대한 최적의 정렬을 얻어내지 못하는 경우가 있다. 본 연구에서는 이러한 dynamic programming의 문제를 해결하기 위하여 genetic algorithm을 복수 염기서열 정렬문제에 적용하였다. 본 논문에서는 genetic algorithm의 design과 적용방법을 기술하였다. 본 연구에서 제안된 genetic algorithm을 사용하여 dynamic programming의 단점이었던 오랜 실행시간을 줄일 수 있었으며, dynamic programming이 제공하지 못하는 최적의 염기서열 정렬을 제공할 수 있었다.

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A Study of Multiple Dynamic Programming (Multiple dynamic programming에 관한 연구)

  • Young Moon park
    • 전기의세계
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    • v.21 no.1
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    • pp.13-16
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    • 1972
  • Dynamic Programming is regarded as a very powerful tool for solving nonlinear optimization problem subject to a number of constraints of state and control variables, but has definite disadvantages that it requires much more computing time and consumes much more memory spaces than other technigues. In order to eliminate the above-mentioned demerits, this paper suggests a news technique called Multiple Dynamic Programming. The underlying principles are based on the concept of multiple passes that, instead of forming fin lattices in time-state plane as adopted in the conventional Dynamic Programming, the Multiple Dynamic Programming constitutes, at the first pass, coarse lattices in the feasible domain of time-state plane and determines the optimal state trajectory by the usual method of Dynamic Programming, and at the second pass again constitutes finer lattices in the narrower domain surrounded by both the upperand lower edges next to the lattice edges through which the first pass optimal trajectory passes and determines the more accurate optimal trajectory of state, and then at the third pass repeats the same processes, and so on. The suggested technique insures remarkable curtailment in amounts of computer memory spaces and conputing time, and its applicability has been demonstrated by a case study on the hydro-thermal power coordination in Korean power system.

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Determination of Work Schedule Type by Dynamic Programming (동적계획모형을 이용한 근무형태 결정)

  • 김중순;안봉근;손달호
    • Korean Management Science Review
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    • v.20 no.2
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    • pp.33-43
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    • 2003
  • In this paper we applied dynamic programming to determining work schedule type. In dynamic programming formulation, each day during a planning horizon represents a stage for which a decision is made. The alternatives are given by work schedule types that combine regular time, overtime, additional shift, and so on. In this case, their associated return function is labor cost. The state is defined as the amount of work time allocated to stage 1, stage 2,…, and current stage. A case study for a real manufacturing company was performed to apply dynamic programming to scheduling daily work hours during a week. The case study showed that total cost of our solution derived from dynamic programming decreased by about 6% as compared with the solution obtained from the previous method.

Approximate Dynamic Programming Strategies and Their Applicability for Process Control: A Review and Future Directions

  • Lee, Jong-Min;Lee, Jay H.
    • International Journal of Control, Automation, and Systems
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    • v.2 no.3
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    • pp.263-278
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    • 2004
  • This paper reviews dynamic programming (DP), surveys approximate solution methods for it, and considers their applicability to process control problems. Reinforcement Learning (RL) and Neuro-Dynamic Programming (NDP), which can be viewed as approximate DP techniques, are already established techniques for solving difficult multi-stage decision problems in the fields of operations research, computer science, and robotics. Owing to the significant disparity of problem formulations and objective, however, the algorithms and techniques available from these fields are not directly applicable to process control problems, and reformulations based on accurate understanding of these techniques are needed. We categorize the currently available approximate solution techniques fur dynamic programming and identify those most suitable for process control problems. Several open issues are also identified and discussed.

A Study on the Dynamic Programming for Control (제어를 위한 동적 프로그래밍에 관한 연구)

  • Cho, Hyang-Duck;Kim, Woo-Shik
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2007.11a
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    • pp.556-559
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    • 2007
  • The notion of linearity is fundamental in science and engineering. Much of system and control theory is based on the analysis of linear system, which does not care whether it is nonlinear and complex. The dynamic programming is one of concerned technology when users are interested in choosing best choice from system operation for nonlinear or dynamic system‘s performance and control problem. In this paper, we will introduce the dynamic programming which is based on discrete system. When the discrete system is constructed with discrete state, transfer between states, and the event to induct transfer, the discrete system can describe the system operation as dynamic situation or symbolically at the logical point of view. We will introduce technologies which are related with controllable of Controlled Markov Chain as shown example of simple game. The dynamic programming will be able to apply to optimal control part which has adaptable performance in the discrete system.

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An Approach of Solving the Constrained Dynamic Programming - an Application to the Long-Term Car Rental Financing Problem

  • Park, Tae Joon;Kim, Hak-Jin;Kim, Jinhee
    • Journal of the Korea Society of Computer and Information
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    • v.26 no.12
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    • pp.29-43
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    • 2021
  • In this paper, a new approach to solve the constrained dynamic programming is proposed by using the constraint programming. While the conventional dynamic programming scheme has the state space augmented with states on constraints, this approach, without state augmentation, represents states of constraints as domains in a contraining programming solver. It has a hybrid computational mechanism in its computation by combining solving the Bellman equation in the dynamic programming framework and exploiting the propagation and inference methods of the constraint programming. In order to portray the differences of the two approaches, this paper solves a simple version of the long-term car rental financing problem. In the conventional scheme, data structures for state on constraints are designed, and a simple inference borrowed from the constraint programming is used to the reduction of violation of constraints because no inference risks failure of a solution. In the hybrid approach, the architecture of interface of the dynamic programming solution method and the constraint programming solution method is shown. It finally discusses the advantages of the proposed method with the conventional method.

An Application of Dynamic Programming to the Selection of Optimal Production Lengths Based on the Minimum Cutting Loss (최소절단손실(最小切斷損失)에 의한 최적생산(最適生産)길이의 선정(選定)에 대한 동적계획법응용(動的計劃法應用))

  • Jo, Gyu-Gap
    • Journal of Korean Institute of Industrial Engineers
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    • v.4 no.2
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    • pp.77-81
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    • 1978
  • The assortment problem with deterministic demand has been formulated so that a dynamic programming can be applied to find optimal production lengths that will minimize the sum of cutting losses. The original minimization problem can be reformulated as the maximization problem with a different objective function. This problem can be solved by the dynamic programming technique. A numerical example illustrates this approach. The ratio of computation amount of emumeration method to that of this dynamic programming is approximately n to 1.

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PORTFOLIO SELECTION WITH NONNEGATIVE WEALTH CONSTRAINTS: A DYNAMIC PROGRAMMING APPROACH

  • Shin, Yong Hyun
    • Journal of the Chungcheong Mathematical Society
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    • v.27 no.1
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    • pp.145-149
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    • 2014
  • I consider the optimal consumption and portfolio selection problem with nonnegative wealth constraints using the dynamic programming approach. I use the constant relative risk aversion (CRRA) utility function and disutility to derive the closed-form solutions.

Stereo Correspondence Algorithm Using Dynamic programming (동적 계획법을 이용한 스테레오 대응 알고리즘)

  • 이충환;홍석교
    • 제어로봇시스템학회:학술대회논문집
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    • 2000.10a
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    • pp.310-310
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    • 2000
  • The main problem in stereo vision is to find corresponding points in left and right image known as correspondence problem. Once correspondences determined, the depth information of those points are easily computed form the pairs of points in both image. In this paper, dynamic programming considering half-occluded region is used fer solving correspondence problem.

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