• Title/Summary/Keyword: 정수 선형 프로그래밍

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Optimal Design for Heterogeneous Adder Organization Using Integer Linear Programming (정수 선형 프로그래밍을 이용한 혼합 가산기 구조의 최적 설계)

  • Lee, Deok-Young;Lee, Jeong-Gun;Lee, Jeong-A;Rhee, Sang-Min
    • Journal of KIISE:Computer Systems and Theory
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    • v.34 no.8
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    • pp.327-336
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    • 2007
  • Lots of effort toward design optimizations have been paid for a cost-effective system design in various ways from a transistor level to RTL designs. In this paper, we propose a bit level optimization of an adder design for expanding its design space. For the bit-level optimization, a heterogeneous adder organization utilizing a mixture of carry propagation schemes is proposed to design a delay-area efficient adder which were not available in an ordinary design space. Then, we develop an optimization method based on Integer Linear Programming to search the expanded design space of the heterogeneous adder. The novelty of the Proposed architecture and optimization method is introducing a bit level reconstruction/recombination of IPs which have same functionality but different speed and area characteristics for producing more find-grained delay-area optimization.

Integer Programming-based Local Search Technique for Linear Constraint Satisfaction Optimization Problem (선형 제약 만족 최적화 문제를 위한 정수계획법 기반 지역 탐색 기법)

  • Hwang, Jun-Ha;Kim, Sung-Young
    • Journal of the Korea Society of Computer and Information
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    • v.15 no.9
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    • pp.47-55
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    • 2010
  • Linear constraint satisfaction optimization problem is a kind of combinatorial optimization problem involving linearly expressed objective function and complex constraints. Integer programming is known as a very effective technique for such problem but require very much time and memory until finding a suboptimal solution. In this paper, we propose a method to improve the search performance by integrating local search and integer programming. Basically, simple hill-climbing search, which is the simplest form of local search, is used to solve the given problem and integer programming is applied to generate a neighbor solution. In addition, constraint programming is used to generate an initial solution. Through the experimental results using N-Queens maximization problems, we confirmed that the proposed method can produce far better solutions than any other search methods.

On Implementing a Hybrid Solver from Constraint Programming and Optimization (제약식프로그래밍과 최적화를 이용한 하이브리드 솔버의 구현)

  • Kim, Hak-Jin
    • Information Systems Review
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    • v.5 no.2
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    • pp.203-217
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    • 2003
  • Constraint Programming and Optimization have developed in different fields to solve common problems in real world. In particular, constraint propagation and linear Programming are their own fundamental and complementary techniques with the potential for integration to benefit each other. This intersection has evoked the efforts to combine both for a solution method to combinatorial optimization problems. Attempts to combine them have mainly focused on incorporating either technique into the framework of the other with traditional models left intact. This paper argues that integrating both techniques into an old modeling fame loses advantages from another and the integration should be molded in a new framework to be able to exploit advantages from both. The paper propose a declarative modeling framework in which the structure of the constraints indicates how constraint programming and optimization solvers can interact to solve problems.

A Study on the Optimization Technique for IC Compaction Problem (IC 밀집화를 위한 최적기술에 대한 연구)

  • Yi, Cheon-Hee
    • Journal of the Korean Institute of Telematics and Electronics
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    • v.26 no.6
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    • pp.115-123
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    • 1989
  • This paper describes a new method of mask compaction to formulate a mixed integer linear programming problem from a user defined stick diagram. By solving this mixed integer program, a compacted and design rule violation free layout is obtained. Also, a new efficient algorithm is given which solves the longest problem in the constraint graph.

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Linear Pseudo Boolean Optimization Approach to Minimum Crosstalk Layer Assignment for Three Layers HVH Gridded Channel Routing Model (선형 의사 불리언 최적화에 근거한 3층 HVH 그리드 채널 배선 모델을 위한 최소 혼신 배선층 할당 방법)

  • Jang, Gyeong-Seon
    • Journal of KIISE:Computer Systems and Theory
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    • v.26 no.12
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    • pp.1458-1467
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    • 1999
  • VLSI 공정 기술이 발달하면서 이웃한 전선 간의 간격이 점점 더 가까워 지고 있으며, 그에 따라 인접 전선 간의 혼신 문제가 심각해지고 있다. 본 논문에서는 3층 그리드 채널 배선에 적용 가능한 혼신을 최소화시키는 배선층 할당 방법을 제안한다. 이 방법은 선형 의사 불린 최적화 기법에 맞도록 고안되었으며, 적절한 변수 선택 휴리스틱과 상한값 추정 방법을 통하여 최적의 결과를 짧은 시간 안에 찾아낸다. 실험 결과를 통하여, 일반적인 0/1 정수 선형 프로그래밍 기법과 비교하여 성능과 수행시간 면에서 우수함을 보인다. Abstract Current deep-submicron VLSI technology appears to cause crosstalk problem severe since it requires adjacent wires to be placed closer and closer. In this paper, we deal with a horizontal layer assignment problem for three layer HVH channel routing to minimize coupling capacitance, a main source of crosstalk. It is formulated in a 0/1 integer linear programming problem which is then solved by a linear pseudo boolean optimization technique. Experiments show that accurate upper bound estimation technique effectively reduces crosstalk in a reasonable amount of running times.

A connection method of LPSolve and Excel for network optimization problem (네트워크 최적화 문제의 해결을 위한 LPSolve와 엑셀의 연동 방안)

  • Kim, Hu-Gon
    • Journal of Korea Society of Industrial Information Systems
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    • v.15 no.5
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    • pp.187-196
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    • 2010
  • We present a link that allows Excel to call the functions in the lp_solve system. lp_solve is free software licensed under the GPL that solves linear and mixed integer linear programs of moderate size. Our link manages the interface between Excel and lp_solve. Excel has a built-in add-in named Solver that is capable of solving mixed integer programs, but only with fewer than 200 variables. This link allows Excel users to handle substantially larger problems at no extra cost. Futhermore, we introduce that a network drawing method in Excel using arc adjacency lists of a network.

On an Implementation of a Hybrid Solver Based on Warren Abstract Machine and Finite Domain Constraint Programming Solver Structures (워렌 추상기계와 한정도메인 제약식프로그램의 구조를 이용한 혼합형 문제해결기 구현에 대한 탐색적 연구)

  • Kim Hak-Jin
    • Journal of Intelligence and Information Systems
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    • v.10 no.2
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    • pp.165-187
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    • 2004
  • Constraint Programming in AS and Optimization in OR started and have grown in different backgrounds to solve common decision-making problems in real world. This paper tries to integrate results from those different fields by suggesting a hybrid solver as an integration framework. Starting with an integrating modeling language, a way to implement a hybrid solver will be discussed using Warren's abstract machine and an finite domain constraint programming solver structures. This paper will also propose some issues rising when implementing the hybrid solver and provide their solutions.

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Extending the Design Space of Adder Architectures and Its Optimization (향상된 설계공간을 갖는 혼합 가산기 구조와 최적화)

  • Lee Deok-Young;Lee Jeong-A;Lee Jeong-Gun;Lee Sang-Min
    • Proceedings of the Korean Information Science Society Conference
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    • 2006.06a
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    • pp.319-321
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    • 2006
  • 본 논문에서는 다양한 캐리 전달 방식(carry propagation scheme)이 단일 가산기 설계를 위하여 복합적으로 사용되는 가산기 구조물 제안하며. 이를 통하여 보다 향상된 delay-area trade-off 점들을 갖는 설계공간을 생성한다. 제안된 구조의 가산기는 각기 다른 캐리전달 방식의 하부 가산기 블록들을 캐리 입/출력 신호를 선형으로 연결한 구조이며, 기존의 단일 캐리전달 방식의 가산기와 달리, 다양한 delay-area trade-off 특성을 갖는 여러 종류의 캐리전달 방식을 비트 수준에서 조합하여 사용함으로써 보다 섬세한 delay-area 설계공간을 생성해낼 수 있다. 그러나, 제안된 가산기 구조의 설계공간은 다양한 캐리전달 방식이 비트 수준에서 할당되므로, 할당가능한 설계 조합은 설계하고자 하는 가산기의 비트 폭과 고려하는 캐리전달 방식의 수에 비례하여 폭발적으로 증가하게 된다. 따라서, 제안된 가산기의 효율적이며, 자동화된 설계공간 탐색 방범이 요구된다. 본 논문에서는 이를 해결하기 위하여 정수 선형 프로그래밍 (Integer Linear Programming, ILP) 방법을 이용하여 제안한 가산기의 최적화 문제를 형식화함으로써 효과적인 설계공간의 탐색 방법을 제안하였다.

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An Improved Function Synthesis Algorithm Using Genetic Programming (유전적 프로그램을 이용한 함수 합성 알고리즘의 개선)

  • Jung, Nam-Chae
    • Journal of the Institute of Convergence Signal Processing
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    • v.11 no.1
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    • pp.80-87
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    • 2010
  • The method of function synthesis is essential when we control the systems not known their characteristic, by predicting the function to satisfy a relation between input and output from the given pairs of input-output data. In general the most systems operate non-linearly, it is easy to come about problem is composed with combinations of parameter, constant, condition, and so on. Genetic programming is proposed by one of function synthesis methods. This is a search method of function tree to satisfy a relation between input and output, with appling genetic operation to function tree to convert function into tree structure. In this paper, we indicate problems of a function synthesis method by an existing genetic programming propose four type of new improved method. In other words, there are control of function tree growth, selection of local search method for early convergence, effective elimination of redundancy in function tree, and utilization of problem characteristic of object, for preventing function from complicating when the function tree is searched. In case of this improved method, we confirmed to obtain superior structure to function synthesis method by an existing genetic programming in a short period of time by means of computer simulation for the two-spirals problem.

Optimization of a System Reliability by Zero-One Programming with GUB Structure (GUB 구조를 갖는 0-1 프로그래밍에 의한 시스템 신뢰성의 최적화)

  • Lee, Jae-Uk;Chun, Koo-Chae;Gen, Mitsuo
    • Journal of the Korean Institute of Telematics and Electronics
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    • v.26 no.2
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    • pp.124-131
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    • 1989
  • This paper showed that the system reliability optimization problem with sereval failure modes at each subsystem can be treated as efficient computational algorithm proposed here. This algorithm was developed by considering a generalized upper bounding which always exists in constraints when the system reliability optimization problem may be reformulated as 0-1 integer programming problem. We demonstrated the optimal allocation of redundant units for system reliability by using the proposed algorithm. Our algorithm is superior to others in terms of number of iterations and variables used.

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