PARTIAL PROXIMAL METHOD OF MULTIPLIERS FOR CONVEX PROGRAMMING PROBLEMS

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概要

• 論文の詳細を見る

• Two variants of the partial proximal method of multipliers are proposed for solving convex programming problems with linear constraints, where the objective function is expressed as the sum of two convex functions. The iteration of each algorithm consists of computing an approximate saddle point of the argumented Lagrangian. The global convergence is established under an approximation criterion for computing the saddle point. In particular, for the convex programming problem with multiple set constraints and the traffic assignment problem, one of the proposed algorithms can effectively be implemented on a parallel computer.

• 社団法人日本オペレーションズ・リサーチ学会の論文

著者

• Fukushima Masao

  Kyoto University

• Ibaraki Satoru

  Department of Applied Mathematics and Physics, Faculty of Engineering, Kyoto University

• Ibaraki Satoru

  Department Of Applied Mathematics And Physics Faculty Of Engineering Kyoto University

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• DUAL-BASED NEWTON METHODS FOR NONLINEAR MINIMUM COST NETWORK FLOW PROBLEMS

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