半正定値計画問題(SDP)に対する主双対内点法の実装と工学的応用について

Bibliographic Information

Other Title
  • The Implementation of the Primal-Dual Interior-Point Method for the Semidefinite Programs and its Engineering Applications

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Description

近年, 半正定値計画問題(SDP)は理論と実用の両面において, 内点法や組合せ最適化, 及び制御理論などの様々な分野で研究されている.SDPA[4]はC++言語で記述されたSDPの標準形を解く主双対内点法のソフトウェアである.SDPAは疎行列を扱うためのデータ構造と, 解くべき問題が大規模で疎構造を持つときに探索方向を効率良く計算する方法[5]を備えている.最後に複数の固有値制約下での構造最適化へのSDPの応用と数値実験結果を報告する.
In resent years, semidefinite program (SDP) has been intensively studies both in theoretical and practical aspects of various fields including interior-point method, combinatorial optimization and the control and systems theory. The SDPA (SemiDefinite Programming Algorithm) [4] is a C++ implementation of a Mehrotra-type primal-dual predictor-corrector interior-point method for solving the standard form semidefinite program. The SDPA incorporates data structures for handling sparse matrices and an efficient method proposed by Fujisawa et al. [5] for computing search directions when problems to be solved are large scale and sparse. Finally, we report numerical experiments of the SDP for the structural optimization under multiple eigenvalue constraints.

Journal

  • IPSJ SIG Notes

    IPSJ SIG Notes 64 9-16, 1998-09-16

    Information Processing Society of Japan (IPSJ)

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Details 詳細情報について

  • CRID
    1571980077093735680
  • NII Article ID
    110002812150
  • NII Book ID
    AN1009593X
  • ISSN
    09196072
  • Text Lang
    en
  • Data Source
    • CiNii Articles

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