1994 Fiscal Year Final Research Report Summary
A Research on Practical Algorithms for Geometrical Optimization Problems with Nonconvex Structure
| Project/Area Number |
05650061
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
Engineering fundamentals
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| Research Institution | University of Tsukuba |
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Principal Investigator |
KUNO Takahito Institute of Information Sciences and Electronics, University of Tsukuba Associate Professor, 電子・情報光学系, 助教授 (00205113)
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| Project Period (FY) |
1993 – 1994
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| Keywords | Mathematical Programming / Global optimization / Nonconvex function / Computational Geometry / Algorithm |
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| Research Abstract |
In this research, we studied some practical algorithme for solving certain classes of geometrical optimization problems with nonconvex structure. We applied global optimization techniques to the problems in the plane and constructed some algorithms for obtaining a globally optimal solution. A few of the results are as follows :
1.Globally optimization of rank-two reverse convex programs : We proposed an algorithm for solving reverse convex program which contains a quasiconcave constraint function defined by two linearly independent n-dimensional vectors. The computational experiments indicated that the algorithm can generate globally optimal solutions very efficiently compared with any existing algorithms.
2.Globally minimization of rank-two saddle functions on a polytope : We proposed an algorithm for minimizing a composite function of a two-dimensional saddle function and n-dimensional affine functions. The computational experiments indicated that the algorithm solves fairly large scale problems.
In addition to the above problems, we studied a class of nonconvex network optimization problems. To solve the problems efficiently, we used computational geometry as some procedures of the algorithm. As a result, we obtained the following :
3.Globally optimization of production-transportation problems : We proposed a pseudopolynomial-time algorithm for solving a class of production-transportation problem.
All the above mentioned algorithms decompose a given problem into several convex subproblems. This solution strategy can also be applied to many other classes of optimization problems.
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